Abstract
Background: SPP1, secreted phosphoprotein 1, is a member of the small
integrin-binding ligand N-linked glycoprotein (SIBLING) family.
Previous studies have proven SPP1 overexpressed in a variety of cancers
and can be identified as a prognostic factor, while no study has
explored the function and carcinogenic mechanism of SPP1 in cervical
cancer.
Methods: We aimed to demonstrate the relationship between SPP1
expression and pan-cancer using The Cancer Genome Atlas (TCGA)
database. Next, we validated SPP1 expression of cervical cancer in the
Gene Expression Omnibus (GEO) database, including [30]GSE7803,
[31]GSE63514, and [32]GSE9750. The receiver operating characteristic
(ROC) curve was used to evaluate the feasibility of SPP1 as a
differentiating factor by the area under curve (AUC) score. Cox
regression and logistic regression were performed to evaluate factors
associated with prognosis. The SPP1-binding protein network was built
by the STRING tool. Enrichment analysis by the R package
clusterProfiler was used to explore potential function of SPP1. The
single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER
database were used to investigate the association between the immune
infiltration level and SPP1 expression in cervical cancer.
Results: Pan-cancer data analysis showed that SPP1 expression was
higher in most cancer types, including cervical cancer, and we got the
same result in the GEO database. The ROC curve suggested that SPP1
could be a potential diagnostic biomarker (AUC = 0.877). High SPP1
expression was associated with poorer overall survival (OS) (P =
0.032). Further enrichment and immune infiltration analysis revealed
that high SPP1 expression was correlated with regulating the
infiltration level of neutrophil cells and some immune cell types,
including macrophage and DC.
Conclusion: SPP1 expression was higher in cervical cancer tissues than
in normal cervical epithelial tissues. It was significantly associated
with poor prognosis and immune cell infiltration. Thus, SPP1 may become
a promising prognostic biomarker for cervical cancer patients.
Keywords: SPP1, biomarker, cervical cancer, prognosis, immune
infiltration
1 Introduction
Cervical cancer remains the fourth most common cancer among women and
accounts for 527,624 new diagnosed cases and 265,672 deaths in 2018
([33]Bray et al. (2018)). Cervical cancer continues to be the first or
second leading cause of cancer-related death among women for many low-
and middle-income countries (LMICs) ([34]Wang et al. (2018)).
Persistent HPV infection, especially types 16 and 18, is a high-risk
factor but not the only one for cervical cancer ([35]Revathidevi et al.
(2020)). Host genetic factors may also be involved in tumor
development. The major treatments for cervical cancer patients include
surgery, chemotherapy, and radiotherapy. For patients with early-stage
cervical cancer, 5-year survival is up to 91.5%, while the treatment of
advanced cervical cancer is not ideal ([36]Luan and Wang (2018)). The
median survival time of metastatic cervical cancer patients is about
8–13 months, and the 5-year overall survival rate is only around 16.5%
([37]Ferlay et al. (2013); [38]van Meir et al. (2014)). Therefore, it
is urgent to find more accurate biomarkers for early detection of
cervical cancer and monitoring the disease progression.
Secreted phosphoprotein 1 (SPP1) is a secreted multifunctional
phosphoprotein located in 4q13 with seven exons and six introns. SPP1,
also known as osteopontin-like protein or early T-lymphocyte activation
1 protein, is a member of the small integrin-binding ligand N-linked
glycoprotein (SIBLING) family which can specifically bind and activate
matrix metalloproteinases (MMPs) in cancer ([39]Su et al. (2020)). Its
main biological functions are involved in immune response,
biomineralization, and tissue remodeling. SPP1 is also related to the
growth, proliferation, migration, apoptosis, and chemotaxis of cells.
Previous studies have proven that SPP1 is overexpressed in a variety of
cancers and can be used to predict the adverse consequences, including
ovarian cancer ([40]Zeng et al. (2018)), glioblastoma ([41]Kijewska et
al. (2017)), hepatocellular carcinoma ([42]Wang et al. (2019)), and
gastric cancer ([43]Song et al. (2019)). Recently, the relationship
between the expression of SPP1 and chemotherapy resistance, such as
prostate cancer and hepatocellular carcinoma, has also attracted the
attention of researchers ([44]Liu et al. (2016); [45]Pang et al.
(2019)), while no study has explored the correlation between SPP1 and
cervical cancer. Therefore, our study aimed to explore the expression
of SPP1 in cervical cancer tissues and its potential clinical values.
In our research, we utilized the cervical cancer RNA-seq data from The
Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and
Genotype-Tissue Expression databases to compare the differential
expression of SPP1 between normal cervical tissues and cervical cancer
samples. Next, we investigated the relationship between SPP1 expression
levels and clinical pathological features of cervical cancer.
Furthermore, we explored the prognostic value of SPP1 in cervical
cancer. Besides, we performed gene enrichment analysis to reveal its
potential functions. Finally, we analyzed the relationship between SPP1
expression and immune infiltration and comprehensively explored its
mechanism in inducing and promoting cervical cancer.
2 Materials and Methods
2.1 RNA Sequencing Data Collection and Analysis
To evaluate the SPP1 expression level in pan-cancer, we downloaded data
from the UCSC Xena ([46]https://xenabrowser.net/datapages/). We
selected samples from the TCGA database for the analysis of SPP1
expression in tumor tissues, while the combined analysis of TCGA and
Genotype-Tissue Expression (GTEx) databases was used for the normal
tissue samples. [47]GSE7803 (Platform: [48]GPL96), [49]GSE63514
(Platform: [50]GPL570), and [51]GSE9750 (Platform: [52]GPL96)
downloaded from GEO were used to obtain cervical cancer microarray
data.
2.2 Correlation and Gene Set Enrichment Analysis
We used data collected from TCGA to perform correlation analysis
between SPP1 and other mRNAs in cervical cancer. To demonstrate the
biological function of SPP1, we selected the top 100 genes most
positively correlated with SPP1 for enrichment analysis. EnrichGO
function in the R package “clusterProfiler” was used to perform gene
ontology (GO) enrichment, including BP, CC, and MF. Kyoto Encyclopedia
of Genes and Genomes (KEGG) analysis was performed using the EnrichKEGG
function of the R package “clusterProfiler.”
2.3 Survival Prognosis Analysis
We used the R package “survival” (version 3.6) to obtain the overall
survival (OS) survival plots of SPP1. Selecting the cutoff value of 50%
as the dividing threshold, the cohorts were divided into
high-expression and low-expression groups. To evaluate the value of
SPP1 in predicting the prognosis of cervical cancer patients, we used
the R package (version 3.6.3) “ROC” for analysis and “ggplot2” for
visual.
2.4 Immune Cell Infiltration Analysis
We used the single-sample GSEA (ssGSEA) method from the R package GSVA
(version 3.6) and Tumor Immune Estimation Resource (TIMER) database
([53]http://timer.cistrome.org/) to comprehensively investigate
molecular characterization of tumor–immune interactions in cervical
cancer. In the literature, we examined the impact of SPP1 expression on
immune cell infiltration using gene expression profiling data. To
investigate the correlation between SPP1 expression and the abundances
of tumor-infiltrating immune cells, p-values were calculated using the
Wilcoxon rank-sum and Spearman’s rank correlation tests.
3 Results
3.1 The mRNA Expression Analysis of SPP1 in Pan-Cancer
Data downloaded from TCGA and GTEx were used to analyze SPP1 expression
in 33 types of cancer. The result revealed that SPP1 was overexpressed
in most cancers, including ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC,
ESCA, GBM, HNSC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD,
READ, SKCM, STAD, TGCT, THCA, THYM, UCEC, and UCS. However, the
expression of SPP1 was low in KICH and KIRC ([54]Figure 1).
Furthermore, we assessed SPP1 expression in cervical cancer in the GEO
database, including [55]GSE7803 (Platform: [56]GPL96), [57]GSE63514
(Platform: [58]GPL570), and [59]GSE9750, and the results confirmed that
SPP1 was overexpressed in cervical cancer tissues ([60]Figures 2A–C).
Additionally, we performed the receiver operating characteristic (ROC)
curve to evaluate the feasibility of the SPP1 expression level to
distinguish cervical cancer tissues from normal cervical tissues. The
area under the ROC curve (AUC) was 0.877, representing the quality of
the test.
FIGURE 1.
[61]FIGURE 1
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SPP1 expression in normal and tumor tissues in TCGA and GTEx databases.
FIGURE 2.
[63]FIGURE 2
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SPP1 expression in the GEO database. (A) SPP1 expression in normal and
tumor tissues in cervical cancer from [65]GSE7803. (B) SPP1 expression
in normal cervical epithelial and cervical cancer tissues from
[66]GSE63514. (C) SPP1 expression in normal cervical tissues and
cervical cancer epithelial component from [67]GSE9750. (D) ROC curve of
SPP1 in cervical cancer. X-axis represents false-positive rates, and
Y-axis represents true-positive rates.
3.2 Clinical Relevance of the SPP1 Expression in Cervical Cancer Patients
The characteristics of 306 primary cervical cancer patients with both
clinical and gene expression data were downloaded from TCGA database.
With the cutoff value of 50% as the dividing threshold, the patients
were divided into a high–SPP1 expression group (n = 153) and a low–SPP1
expression group (n = 153). The correlation of the SPP1 expression
level and patients’ clinicopathologic characteristics was explored. We
found that SPP1 expression was significantly associated with T stage (P
= 0.02), clinical stage (P = 0.02), and histologic type (P
[MATH: < :MATH]
0.001) by using the chi-square test or Fisher’s exact test. The
Wilcoxon rank-sum test revealed that SPP1 expression was associated
with age (P = 0.038) ([68]Table 1).
TABLE 1.
Correlation analyzed between SPP1 expression and clinicopathologic
characteristics in cervical cancer based on TCGA database.
Characteristic Low expression of SPP1 High expression of SPP1 p value
N 153 153
T stage, n (%) 0.020
T1 82 (33.7%) 58 (23.9%)
T2 31 (12.8%) 41 (16.9%)
T3 6 (2.5%) 15 (6.2%)
T4 4 (1.6%) 6 (2.5%)
N stage, n (%) 0.243
N0 73 (37.4%) 61 (31.3%)
N1 27 (13.8%) 34 (17.4%)
M stage, n (%) 0.699
M0 55 (43.3%) 61 (48%)
M1 4 (3.1%) 7 (5.5%)
Clinical stage, n (%) 0.020
Stage I 95 (31.8%) 67 (22.4%)
Stage II 30 (10%) 39 (13%)
Stage III 17 (5.7%) 29 (9.7%)
Stage IV 9 (3%) 13 (4.3%)
Radiation therapy, n (%) 0.726
No 63 (20.6%) 59 (19.3%)
Yes 90 (29.4%) 94 (30.7%)
Primary therapy outcome, n (%) 0.106
PD 7 (3.2%) 16 (7.3%)
SD 2 (0.9%) 4 (1.8%)
PR 4 (1.8%) 4 (1.8%)
CR 101 (46.1%) 81 (37%)
Race, n (%) 0.444
Asian 12 (4.6%) 8 (3.1%)
Black or African American 13 (5%) 18 (6.9%)
White 106 (40.6%) 104 (39.8%)
Histologic type, n (%) <0.001
Adenosquamous 40 (13.1%) 13 (4.2%)
Squamous cell carcinoma 113 (36.9%) 140 (45.8%)
Histologic grade, n (%) 0.954
G1 10 (3.6%) 9 (3.3%)
G2 69 (25.2%) 66 (24.1%)
G3 62 (22.6%) 57 (20.8%)
G4 0 (0%) 1 (0.4%)
Age (years), median (IQR) 45 (37, 54) 49 (40, 60) 0.038
[69]Open in a new tab
We conducted the logistic regression method to further analyze the
relationship between the SPP1 expression level and the
clinicopathologic characteristics of cervical cancer. The results
showed that the expression level of SPP1 was significantly associated
with T stage (P = 0.004), clinical stage (P = 0.002), primary therapy
outcome (P = 0.033), histologic type (P
[MATH: < :MATH]
0.001), and age (P = 0.019) ([70]Table 2).
TABLE 2.
SPP1 expression associated with clinicopathologic characteristics by
logistic regression.
Characteristic Total (N) Odds ratio (OR) p value
T stage (T2 and T3 and T4 vs. T1) 243 2.138 (1.278–3.609) 0.004
N stage (N1 vs. N0) 195 1.507 (0.821–2.786) 0.187
M stage (M1 vs. M0) 127 1.578 (0.451–6.294) 0.485
Clinical stage (Stage II and Stage III and Stage IV vs. Stage I) 299
2.051 (1.295–3.269) 0.002
Primary therapy outcome (SD and PR and CR vs. PD) 219 0.364
(0.135–0.893) 0.033
Histologic type (squamous cell carcinoma vs. adenosquamous) 306 3.812
(1.993–7.732) <0.001
Age (>50 vs. ≤50 years) 306 1.743 (1.097–2.787) 0.019
Radiation therapy (yes vs. no) 306 1.115 (0.706–1.765) 0.641
Histologic grade (G2 and G3 and G4 vs. G1) 274 1.052 (0.411–2.731)
0.916
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Association Between SPP1 Expression and Cancer Patient Survival Prognosis
We performed univariate and multivariate Cox analyses of overall
survival (OS) in cervical cancer patients, and results are shown in
[72]Table 3. In univariate Cox analysis of SPP1, T stage (P = 0.025), N
stage (P = 0.002), M stage (P = 0.023), and SPP1 expression (P = 0.032)
were associated with overall survival (OS) in cervical cancer patients.
In the multivariate Cox model, we found that N stage (P = 0.002) and
SPP1 expression (P = 0.045) were still relevant to worse prognosis.
Furthermore, we investigated the relationship between SPP1 expression
and overall survival (OS) of cervical cancer patients. According to the
KM plot, patients with higher SPP1 mRNA expression showed poorer
prognosis than the lower group (HR = 1.69, 95% CI: 1.05–2.72, P =
0.032) ([73]Figure 3). Thus, SPP1 may become a promising prognostic
biomarker for cervical cancer patients.
TABLE 3.
Univariate and multivariate Cox analyses of prognostic factors in
cervical cancer.
Characteristic Total (N) Univariate analysis Multivariate analysis
Hazard ratio (95% CI) p value Hazard ratio (95% CI) p value
T stage (T2 and T3 and T4 vs. T1) 243 1.906 (1.085–3.348) 0.025 1.193
(0.419–3.395) 0.741
N stage (N1 vs. N0) 195 2.844 (1.446–5.593) 0.002 3.117 (1.517–6.403)
0.002
M stage (M1 vs. M0) 127 3.555 (1.187–10.641) 0.023
TP53 (high vs. low) 306 0.854 (0.537–1.356) 0.503
Clinical stage (Stage II and Stage III and Stage IV vs. Stage I) 299
1.462 (0.920–2.324) 0.108 0.464 (0.160–1.345) 0.157
Radiation therapy (yes vs. no) 306 1.172 (0.694–1.981) 0.553
Race (Black or African American and White vs. Asian) 261 1.537
(0.374–6.317) 0.552
Age (>50 vs. ≤50 years) 306 1.289 (0.810–2.050) 0.284 0.658
(0.298–1.452) 0.299
Histologic type (squamous cell carcinoma vs. adenosquamous) 306 1.033
(0.543–1.969) 0.920
Histologic grade (G2 and G3 vs. G1) 273 1.212 (0.378–3.882) 0.746
SPP1 (high vs. low) 306 1.686 (1.046–2.719) 0.032 2.207 (1.019–4.777)
0.045
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The value in bold indicates that p is less than 0.05, which is
meaningful.
FIGURE 3.
FIGURE 3
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Association between SPP1 expression and OS in cervical cancer patients.
3.4 Correlation and SPP1-Related Gene Enrichment Analysis
In this study, we only considered physically binding protein
interactions and obtained 50 experimental supported SPP1-binding
proteins from the STRING network ([76]Figure 4). We downloaded data
from TCGA database to further investigate the function of SPP1 and
search SPP1 expression–correlated genes for related pathway analysis.
We obtained the top 100 most positively correlated genes with SPP1 for
GO and KEGG enrichment analysis by the “clusterProfile” R package. The
GO analysis data showed that most of the genes were associated with
neutrophil degranulation, neutrophil activation involved in immune
response, neutrophil activation, and neutrophil-mediated immunity
([77]Figure 5A). The KEGG data suggested that the “phagosome” may be
related to the carcinogenic mechanism of SPP1 ([78]Figure 5B).
FIGURE 4.
[79]FIGURE 4
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SPP1-binding proteins obtained by the STRING tool.
FIGURE 5.
[81]FIGURE 5
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Function and pathway enrichment analysis of SPP1 in cervical cancer.
(A) Significant Gene Ontology terms (including BP, MF, and CC) of the
top 100 genes most positively associated with SPP1. (B) Significant
KEGG pathway of the top 100 genes most positively associated with SPP1.
3.5 Relationship Between SPP1 Expression and Immune Cell Infiltration
Through the previous enrichment analysis, we found that SPP1 was mainly
related to neutrophils and phagosomes. We hypothesized that there might
be some relationship between SPP1 and immune cells. Thus, we further
assessed whether the SPP1 expression level was associated with immune
cell infiltration. We used ssGSEA from the R package with Spearman’s r
to investigate the potential association between the SPP1 expression
level and 24 types of immune cells. The result revealed that SPP1
expression had significant correlation with iDC, macrophages,
neutrophils, NK CD56 bright cells, Th1 cells, DC, pDC, mast cells, and
Treg cells ([83]Figure 6). Further research showed that SPP1 expression
was positively correlated with infiltration levels of iDC ([84]Figure
7A) (r = 0.250, P
[MATH: < :MATH]
0.001), macrophages ([85]Figure 7B) (r = 0.480, P
[MATH: < :MATH]
0.001), neutrophils ([86]Figure 7C) (r = 0.180, P = 0.002), Th1 cells
([87]Figure 7E) (r = 0.160, P = 0.006), DC ([88]Figure 7F) (r = 0.150,
P = 0.007), and Treg cells ([89]Figure 7I) (r = 0.110, P = 0.046). In
contrast, SPP1 expression was negatively correlated with that of NK
CD56 bright cells ([90]Figure 7D) (r = −0.170, P = 0.003), pDC
([91]Figure 7G) (r = −0.130, P = 0.026) and mast cells ([92]Figure 7H)
(r = −0.130, P = 0.028). This prompted us to examine the relationship
between the SPP1 expression level and immune infiltration.
Surprisingly, we found significant differences in infiltrating immune
cell levels, including iDC, macrophages, neutrophils, NK CD56 bright
cells, Th1 cells, DC, and pDC (P
[MATH: < :MATH]
0.05), when SPP1 expression was categorized into high and low groups
([93]Figures 8A–G), while no significant difference in mast cells and
Treg cells was noted ([94]Figures 8H,I). Finally, we assessed the
impact of immune cell infiltration on clinical survival outcome of
cervical cancer patients by TIMER ([95]http://timer.cistrome.org/). We
found that high levels of macrophages and DC cells were associated with
poor prognosis of cervical cancer patients (P
[MATH: < :MATH]
0.05) ([96]Figures 9A,B).
FIGURE 6.
[97]FIGURE 6
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(A) Lollipop chart of SPP1 expression level in 24 immune cells. (B) The
immune cell infiltration associated with SPP1 expression, P < 0.05,
represents a significant result.
FIGURE 7.
[99]FIGURE 7
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Correlation between SPP1 expression and immune cell infiltration. (A–I)
Correlation between SPP1 expression and iDC, macrophages, neutrophils,
NK CD56 bright cells, Th1 cells, DC, pDC, mast cells, and Treg cells.
FIGURE 8.
[101]FIGURE 8
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Comparison of immune cells between high– and low–SPP1 expression
groups. (A–I) Histogram showing the difference of iDC, macrophages,
neutrophils, NK CD56 bright cells, Th1 cells, DC, pDC, mast cells, and
Treg cell infiltration level between high–and low–SPP1 expression
groups.
FIGURE 9.
[103]FIGURE 9
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Impact of immune cell infiltration on prognosis in cervical cancer
patients. (A) Clinical survival outcome of cervical cancer patients in
the high-macrophage group. (B) Clinical survival outcome of cervical
cancer patients in the high–DC cell group.
4 Discussion
Invasive cervical cancer remains the leading cause of cancer death
among women worldwide ([105]Shen et al. (2020)). Thus, it is necessary
to find more accurate biomarkers to detect at an early stage and
monitor disease progression. According to the previous studies, SPP1 is
overexpressed in various cancer types ([106]Xu et al. (2017); [107]Choe
et al. (2018); [108]Zhang et al. (2020)) and identified as a prognostic
factor ([109]Li et al. (2018); [110]Chen J et al. (2019); [111]Guo et
al. (2020)), while to our knowledge, no study has explored the
relationship of SPP1 expression and cervical cancer. In our study, we
attempted to explore the potential mechanism of SPP1 in promoting
cervical cancer and its feasibility as a molecular biomarker.
In pan-cancer analysis, we found that SPP1 was upregulated in most
cancer types. Further exploration revealed that higher SPP1 expression
was associated with reduced overall survival (OS) in cervical cancer
patients. We performed logistic regression to evaluate the relationship
between the SPP1 expression level and the clinicopathologic
characteristics of cervical cancer. The result showed that SPP1 was
significantly correlated with clinical stages. In addition, univariate
and multivariate Cox analyses indicated that SPP1 was an independent
factor to predict prognosis of patients. All these aforementioned
results and ROC analysis suggest that SPP1 may be a promising
prognostic biomarker for cervical cancer patients.
The tumor microenvironment (TME), composed of various types of immune
cells, played an important role in tumor progression, metastasis, and
treatment resistance ([112]Usui et al. (2016)). The composition of
tumor-infiltrating immune cells strongly influenced the tumor
microenvironment and the behavior of the tumor. Our gene enrichment
analysis revealed that the main biological function of SPP1 was mainly
involved in immune response. We next confirmed that SPP1 expression
correlated with immune cell infiltration. Hence, we hypothesized that
SPP1 may affect the tumor microenvironment by changing proportions of
specific immune cell types, thereby promoting tumor progression and
metastasis. It was, indeed, the case that SPP1 had recently been shown
to be an important component in maintaining the tumor microenvironment
in AML ([113]Ruvolo et al. (2019)). Our research demonstrated the
significant positive correlation between macrophages and the expression
of SPP1. Macrophages are important components of the tumor
microenvironment, and tumor-associated macrophages play complex roles
in cancer pathophysiology ([114]Gibson et al. (2019)). A previous study
found that SPP1 was involved in the function, migration, and
differentiation of macrophages ([115]Zhang et al. (2017); [116]Wei et
al. (2019); [117]Jaitin et al. (2019); [118]Srirussamee et al. (2019)).
A recent study also showed that SPP1 was essential for M2-like
macrophage, the tumor-associated macrophage, and promoted tumor growth
([119]Chen P et al. (2019)). Furthermore, we found that the increased
level of macrophages and DC infiltration were correlated with poor
prognosis. Our results were supported by the findings of similar
studies about this topic ([120]Long et al. (2016); [121]Ndiaye et al.
(2019)). Certainly, the tumor microenvironment had a high level of
complexity in its regulation; other immune cell types in the tumor
microenvironment may also influence tumor cell survival, including iDC,
neutrophils, NK CD56 bright cells, Th1 cells, DC, and pDC. Future
studies were needed to further explore the relationship between SPP1
expression and these cells.
In conclusion, we demonstrated that SPP1 expression was upregulated in
cervical cancer and significantly related to poor survival outcome. In
addition to this, SPP1 might participate in the occurrence and
development of cervical cancer by influencing the infiltration level of
immune cells. Therefore, our study revealed the role of SPP1 in
cervical cancer and identified a promising prognostic biomarker.
Although our study is the first work to explore the relationship
between SPP1 expression and cervical cancer, it also has some
limitations. First, all of the data analyzed by bioinformatics methods
in this study were downloaded directly from public databases, so it
requires further validation by experimental investigations; second, the
number of normal samples used as controls was considerably different
from that of patients with tumor in the TCGA database; therefore,
further studies based on an equal balance of sample size are necessary.
Third, further validation studies with a long-term follow-up and larger
cohorts of patients are needed to definitely validate SPP1 as an OS
predictor. Last but not least, our study laid the foundation for
detailed studies of the correlation between SPP1 and the
tumor-associated immune microenvironment. However, more studies are
required to explore the hypothesis in depth.
Statement
The cervical cancer cell lines (Siha and Hela) present in this study
were obtained from the Scientific Research Center of Zhongnan Hospital
of Wuhan University. And normal cervical epithelial cell (END1) was
donated by Wuhan University Basic Medical College.
Data Availability Statement
Publicly available datasets were analyzed in this study. These data can
be found freely from TCGA data portal
([122]https://portal.gdc.cancer.gov/) and GEO database
([123]https://www.ncbi.nlm.nih.gov/geo/).
Author Contributions
KZ and WZ contributed to the study conception and design. Material
preparation, data collection, and analysis were performed by KZ and ZM.
KZ contributed to the literature search. The first draft of the
manuscript was written by KZ, and all authors commented on previous
versions of the manuscript. WZ reviewed the article and gave
suggestions on the revision of the article. All authors read and
approved the final manuscript.
Funding
Our research was supported by the project of improving the ability of
diagnosis and treatment of difficult diseases in Zhongnan Hospital of
Wuhan University. The project number is ZLYNXM202019.
Conflict of Interest
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.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors, and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Glossary
aDC
activated DC
ACC
adrenocortical carcinoma
BLCA
bladder urothelial carcinoma
BRCA
breast invasive carcinoma
CESC
cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL
cholangiocarcinoma
COAD
colon adenocarcinoma
DLBC
lymphoid neoplasm diffuse large B-cell lymphoma
ESCA
esophageal carcinoma
GBM
glioblastoma multiforme
GEO
Gene Expression Omnibus
GO
Gene Ontology
HNSC
head and neck squamous cell carcinoma
iDC
immature DC
KICH
kidney chromophobe
KIRC
kidney renal clear cell carcinoma
KIRP
kidney renal papillary cell carcinoma
KEGG
Kyoto Encyclopedia of Genes and Genomes
LAML
acute myeloid leukemia
LGG
lower grade glioma
LIHC
liver hepatocellular carcinoma
LUAD
lung adenocarcinoma
LUSC
lung squamous cell carcinoma
OS
overall survival
OV
ovarian serous cystadenocarcinoma
pDC
plasmacytoid DC
PAAD
pancreatic adenocarcinoma
PRAD
prostate adenocarcinoma
READ
rectum adenocarcinoma
SKCM
skin cutaneous melanoma
STAD
stomach adenocarcinoma
SPP1
secreted phosphoprotein 1
Tcm
T central memory
Tem
T effector memory
Tfh
T follicular helper
Tgd
T gamma delta.
TCGA
The Cancer Genome Atlas
TGCT
testicular germ cell tumor
THCA
thyroid carcinoma
THYM
thymoma
UCEC
uterine corpus endometrial carcinoma
UCS
uterine carcinosarcoma
References