Abstract
Head and neck squamous cell carcinoma (HNSCC) is a common malignancy
worldwide with a poor prognosis. DNA methylation is an epigenetic
modification that plays a critical role in the etiology and
pathogenesis of HNSCC. The current study aimed to develop a predictive
methylation signature based on bioinformatics analysis to improve the
prognosis and optimize therapeutic outcome in HNSCC. Clinical
information and methylation sequencing data of patients with HNSCC were
downloaded from The Cancer Genome Atlas database. The R package was
used to identify differentially methylated genes (DMGs) between HNSCC
and adjacent normal tissues. We identified 22 DMGs associated with 246
differentially methylated sites. Patients with HNSCC were classified
into training and test groups. Cox regression analysis was used to
build a risk score formula based on the five methylation sites
(cg26428455, cg13754259, cg17421709, cg19229344, and cg11668749) in the
training group. The Kaplan–Meier survival curves showed that the
overall survival (OS) rates were significantly different between the
high‐ and low‐risk groups sorted by the signature in the training group
(median: 1.38 vs. 1.57 years, log‐rank test, p < 0.001). The predictive
power was then validated in the test group (median: 1.34 vs.
1.75 years, log‐rank test, p < 0.001). The area under the receiver
operating characteristic curve (area under the curve) based on the
signature for predicting the 5‐year survival rates, was 0.7 in the
training and 0.73 in test groups, respectively. The results of
multivariate Cox regression analysis showed that the riskscore (RS)
signature based on the five methylation sites was an independent
prognostic tool for OS prediction in patients. In addition, a
predictive nomogram model that incorporated the RS signature and
patient clinical information was developed. The innovative methylation
signature‐based model developed in our study represents a robust
prognostic tool for guiding clinical therapy and predicting the OS in
patients with HNSCC.
Keywords: DNA methylation, HNSCC, nomogram, prognosis, risk score
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1. BACKGROUND
Head and neck squamous cell carcinoma (HNSCC) is the sixth most cancer
worldwide and a collective term for cancers closely associated with
squamous differentiation in the head and neck.[42]^1 HNSCC commonly
mainly consists of a group of tumors derived from the mucosal surfaces
of four major anatomical sites: oral cavity, pharynx, larynx, and
sinonasal cavity.[43]^2 Owing to the lack of effective tools for
clinical risk assessment and early diagnosis, HNSCC is often not
discovered until it has progressed into advanced stages in more than
half of the patients. According to a 2019 report, 600,000 new HNSCC
cases are discovered per annum, with a mortality rate of over 50%
worldwide.[44]^3 The latest statistics show that the incidence of HNSCC
in China is 3.628%, suggesting an increasing trend and presentation at
a younger age. The risk factors for pathogenesis of HNSCC include
unique genetic backgrounds, tobacco and alcohol consumption, and viral
infections, such as human papilloma virus (HPV) and Epstein–Barr
virus.[45]^4, [46]^5, [47]^6, [48]^7, [49]^8 Despite the advances in
chemo‐ and radiotherapeutic treatment modalities, the overall 5‐year
survival rate for HNSCC remains low.[50]^9 Although treatments
administered at an early stage are effective in terms of development of
fewer lymphatic metastases, HNSCC is often diagnosed at advanced
stages. Therefore, there is an urgent need to identify more effective
diagnostic biomarkers to guide clinical therapy and prognostic
evaluation to increase the survival rates of patient.
DNA methylation is an epigenetic modification that occurs at
cytosine‐phosphate‐guanine (CpG) dinucleotides and is critical for
cancer development and progression. This modification has been shown to
play an essential role in regulating gene expression, RNA processing,
and protein function. Some studies have demonstrated that aberrant DNA
methylation is a common and early event in HNSCC that precedes
malignant cell proliferation.[51]^10 As the DNA methylation genes are
closely associated with tumor metastasis and invasion, detection of
these genes with the high accuracy has immense significance in the
early diagnosis of HNSCC.[52]^11, [53]^12 With the development of
high‐throughput technologies, a series of aberrant DNA methylation
genes, including p16, p15, p14, DAPK, and E‐cadherin, have been
identified as differentially expressed genes in HNSCC.[54]^13, [55]^14,
[56]^15, [57]^16 Recently, instead of using single methylation gene or
combinations of multiple genes to evaluate the early diagnosis of
HNSCC, researchers have begun to comprehensively investigate the
methylation and expression profiles of genes involved in HNSCC and
evaluate their predictive values in the prognosis of HNSCC. Ma et al.
developed a four‐gene methylation signature consisting of ZNF10,
TMPRSS12, ERGIC2, and RNF215 to predict the survival outcomes of
patients with HNSCC.[58]^17 The identification of tumor‐specific
methylation sites is critical for the early detection and prognosis of
cancer.[59]^18 These sites are usually overexpressed in cancer cells,
unlike in normal cells. These findings indicate that such
tumor‐specific methylation sites have considerable potential for cancer
screening.[60]^19
To date, very little research has been conducted in the use of
methylation sites as a signature for predicting the prognosis of HNSCC.
To investigate potential DNA methylation sites associated with HNSCC
survival, a novel model based on five methylation sites signature was
established in the current study. This model was used to evaluate both
the training and test groups of 512 patients with HNSCC, and improve
survival prediction of HNSCC.
2. METHODS
2.1. Data retrieval and analysis
In this study, we utilized the tool of The Cancer Genome Atlas
(TCGA)‐Assembler to download the clinical and DNA methylation data of
patients with HNSCC collected from the Illumina Infinium Human
Methylation 450 Platform (San Diego, CA) from TCGA databank
([61]https://portal.gdc.cancer.gov/).[62]^20 Methylation information
pertaining to 529 HNSCC tissue samples and 50 adjacent normal tissue
samples was collected from the level three methylation database. These
data were preprocessed by TCGA pipelines in the form of β values
calculated as M/(M + U), where M represents the methylated probe
intensity and U represents the unmethylated probe intensity.
Approximately 512 samples from patients with intact medical records
(gender, age, tumor grade, clinical stage, and vital status) and
methylated information were randomized into two groups: a training
group with 341 samples (to identify and construct prognostic
biomarkers) and a test group with the remaining 171 samples (to verify
the accuracy of the prognostic biomarkers).
2.2. Identification and functional enrichment analysis of the differentially
methylated genes
The differentially methylated genes (DMGs) were screened using four
steps as follows: First, we downloaded the TCGA‐HNSC.methylation450.tsv
file, which contains genome‐wide methylation site β values. Second, we
downloaded the probe_full_annotation.txt file from the website
([63]http://lifeome.net/software/fastdma/annotation/probe_full_annotati
on.txt) that contains the location of the methylation site in relation
to the gene. Third, we calculated the average methylation of β within a
gene. Fourth, the R (Version:3.5.1) package limma was applied to
compare the mean β value of genes between the 529 HNSCC tissue samples
and 50 adjacent normal tissue samples. Genes with p values and false
discovery rates < 0.05 were considered to be DMGs. Then the
corresponding methylation sites of the DMGs were retrieved for further
analysis. To further study the functions of survival‐related DMGs, we
performed gene ontology (GO) and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway analysis ([64]https://www.genome.jp/kegg/) to
investigate the roles of all 22 DMGs based on the R package
“clusterProfiler”. p < 0.1 was set as the cutoff criterion.
2.3. Construction of a five‐methylation site signature in the training group
Univariate Cox proportional hazards regression analysis was performed
to identify the methylation sites related to the overall survival (OS)
rate of patients’ by setting p < 0.05. The candidate markers were
evaluated by Cox multivariate analysis to screen out the most effective
predictive diagnostic and prognostic sites, which were subsequently
used to construct the following model to assess the prognosis risk:
[MATH: Riskscore
(RS)=∑iN
(expression∗coeffi
cient) :MATH]
The Riskscore (RS) represents the risk score of patients with HNSCC,
and N denotes the representative number of prognostic methylation
sites. Expression refers to the expression value of methylation sites,
and the coefficient refers to the regression coefficient of methylation
sites, representing the contribution of methylation sites to the
prognostic RS. Patients were separated into high‐ and low‐risk groups
using the median RS from the training group as the cutoff point.
Subsequently, a RS system was established, in which patients with an RS
value higher than the median were assigned to the high‐risk group,
whereas those with RS values lower than the median were assigned to the
low‐risk group.
2.4. Statistical analysis
Kaplan–Meier survival curves were calculated using the R software's
survival package to estimate the survival time and compare the survival
probabilities between the high‐risk and low‐risk groups.[65]^21,
[66]^22 Subsequently, the time‐dependent receiver operating
characteristic (ROC) curve was applied to assess the specificity and
sensitivity with the survival prediction RS of the five methylation
sites in the training group. The area under the curve (AUC) was then
calculated. Prognostic DNA methylation site signatures were constructed
by comparing the AUC values in the training group. Subsequently, the
prognostic performance of the five‐methylation site signature was
assessed in the test group based on the AUC values and results of the
Kaplan–Meier survival analysis. The evaluated association between the
expression levels of the five methylation sites and possibility of
patient survival was constructed using a nomogram in the training group
based on multivariable analysis.
2.5. Development of the predictive nomogram model
Of the 512 samples from patients with HNSCC, 289 were randomly selected
based on the medical records to develop a predictive nomogram as per
the “Iasonos” guidelines,[67]^23, [68]^24 to predict the 1‐, 2‐, and
3‐year survival rates of patients with HNSCC. Nomogram was established
by combining RS with clinical variables (age, gender, grade, and state)
using a multivariable Cox regression model. In this model, Cox
regression was performed to identify whether RS was an independent
survival predictor to assess the survival rate of patients with HNSCC.
The above‐mentioned analyses were conducted using the R program
(version 3.5.1): survival random forest, limma, and ROC packages
(Bioconductor, [69]http://www.bioconductor.org/). Statistical analysis
was also performed using the R program, and statistical significance
was set at p < 0.05.
3. RESULTS
3.1. Patient characteristics
A total of 528 patients with HNSCC were enrolled in this study. Based
on the tumor‐node‐metastasis (TNM) staging system classification, the
clinical stage of the tumors were classified into stages I–IV. Gender,
age, grade, stage, and vital status were introduced as variables in
this study. Patients lacking records for any one of these variables
were excluded from the study. A total of 512 patients with HNSCC were
randomly divided into training and test groups comprising of 341 and
171 patients, respectively. The clinicopathological characteristics of
the 512 patients with HNSCC categorized by stage are summarized in
Table [70]1. The tumor origin information of the 579 tissues from the
TCGA database are shown in Table [71]2. Schematic of the technical
route used in this study is illustrated in Figure [72]1. The five
methylation sites that are closely associated with the OS of patients
with HNSCC were identified in the training group and validated in the
test group.
TABLE 1.
Summary of patient demographics and characteristics
Characteristic Training (N = 341) Test (N = 171)
Gender
Female 93 (27.3%) 43 (25.1%)
Male 248 (72.7%) 128 (74.9%)
Age (years)
<60 146 (42.8%) 84 (49.1%)
≥60 195 (57.2%) 87 (50.9.%)
Grade
1 41 (12.0%) 17 (10.0%)
2 204 (59.8%) 100 (58.5%)
3 76 (22.3%) 43 (25.1%)
4 3 (0.8%) 4 (2.3%)
Stage
I 15 (4.4%) 12 (7.0%)
II 42 (12.3%) 26 (15.2%)
III 114 (33.4%) 25 (14.6%)
IV 181 (53.1%) 80 (46.8%)
Vital status
Living 214 (62.6%) 106 (61.9%)
Dead 127 (37.4%) 65 (38.1%)
[73]Open in a new tab
TABLE 2.
The origins information of HNSCC and adjacent normal tissues from TCGA
database used in this research
Site HNSCC Normal
Border of tongue 1 0
Mandible 1 0
Palate 1 0
Pharynx 1 0
Posterior wall of oropharynx 1 0
Retromolar area 1 0
Supraglottis 1 0
Upper gum 1 0
Ventral surface of tongue 1 0
Anterior floor of mouth 2 0
Lower gum 2 0
Lip 3 0
Hard palate 4 1
Gum 8 0
Hypopharynx 9 0
Oropharynx 9 0
Cheek mucosa 19 0
Mouth 22 0
Base of tongue 24 2
Tonsil 47 0
Floor of mouth 55 4
Overlapping lesion of lip, oral cavity, and pharynx 70 5
Larynx 116 16
Tongue 130 22
[74]Open in a new tab
Abbreviations: HNSCC, head and neck squamous cell carcinoma; TCGA, The
Cancer Genome Atlas.
FIGURE 1.
FIGURE 1
[75]Open in a new tab
The flow chart of the study
3.2. Functional enrichment analysis of DMGs in HNSCC
We identified 22 DMGs in the methylation profile (Table [76]3),
corresponding to 246 methylated sites (Figure [77]2). To gain further
insights into the potential functions of the 22 identified DMGs, GO and
KEGG pathway analyses were performed to investigate their biological
functions. The top significant terms of in the GO analysis
(Figure [78]3A) showed that the DMGs involved in HNSCC were mainly
enriched in “forebrain development,” “hypothalamus development,” and
“diencephalon development” terms, which indicated that the DMGs may
play a vital role in head and neck development. KEGG analysis
(Figure [79]3B) showed that the DMGs were enriched in pathways
associated with carcinogenesis, such as “Herpes simplex virus 1
infection” and “viral carcinogenesis.”
TABLE 3.
The twenty‐two differentially methylated genes
Gene Normal mean β value Tumor mean β value logFC p value
TMEM215 0.1127 0.2978 1.4020 7.94E − 26
CTD‐2537O9.1 0.0860 0.2075 1.2716 3.44E − 14
NKX2‐3 0.1255 0.2572 1.0354 1.11E − 17
[80]AL133410.1 0.0284 0.0608 1.0995 2.45E − 05
RP11‐443N24.2 0.4964 0.2472 −1.0057 4.11E − 15
HIST1H2BE 0.0985 0.1977 1.0049 1.48E − 10
NHEG1 0.0187 0.0442 1.2397 1.84E − 06
SOX3 0.1827 0.4806 1.3955 2.63E − 22
NEUROG3 0.0722 0.1844 1.3517 1.97E − 26
ACTA1 0.1714 0.3741 1.1259 1.02E − 29
MIR124‐2HG 0.1937 0.3925 1.0191 8.97E − 29
RP11‐1006G14.2 0.0781 0.1791 1.1982 2.33E − 06
POU3F4 0.2028 0.4409 1.1205 7.42E − 24
RP11‐21L23.2 0.0124 0.0408 1.7158 0.002556851
ZNF730 0.0722 0.2428 1.7492 1.26E − 24
RAB39A 0.0576 0.1429 1.3111 8.71E − 20
NKX2‐6 0.1501 0.3851 1.3589 4.55E − 30
RNASEH1P2 0.0883 0.2091 1.2439 1.62E − 13
RP5‐1103G7.10 0.0619 0.1709 1.4648 3.13E − 06
RP11‐685G9.2 0.2024 0.4104 1.0198 1.20E − 19
ZNF729 0.1480 0.3288 1.1517 8.28E − 26
ZNF702P 0.1668 0.3503 1.0703 3.04E − 28
[81]Open in a new tab
FIGURE 2.
FIGURE 2
[82]Open in a new tab
Heat map of the differentially expressed methylated genes between HNSCC
samples and corresponding normal tissues. HNSCC, head and neck squamous
cell carcinoma
FIGURE 3.
FIGURE 3
[83]Open in a new tab
GO (A) and KEGG (B) pathway enrichment analysis of 22 DMGs involved in
HNSCC. The top significant terms were shown in the heat map. Different
color indicates the p value of different items. DMG, differentially
methylated gene; GO, Gene Ontology; HNSCC, head and neck squamous cell
carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes
3.3. Selection of candidate prognostic methylated sites in the training group
We then screened 512 patients with complete clinical information,
including gender, age, and clinical stage, and randomly divided them
into a training group (n = 341) and a test group (n = 171). Univariate
Cox proportional hazards regression analysis was used to explore the
relationship in the training group between OS and 246 differentially
methylated sites identified above. A total of 19 methylated sites were
found to be significantly correlated with OS (p < 0.05, Figure [84]4A).
The most predictive differentially expressed methylated sites that were
strongly correlated with patient survival were then screened out from
the 19 methylation sites using Cox multivariate analysis. An optimal
predictive model for HNSCC prognosis, composed of cg26428455,
cg13754259, cg17421709, cg19229344, and cg11668749 was constructed with
the minimum Akaike information criterion, p < 0.05, and Harrel's
concordance index (C‐index) of the model for OS prediction of 0.66 (see
Figure [85]4B). Three methylation sites were presumed to be risk
factors (hazard ratio [HR] > 1), whereas two were protective factors
(HR < 1) and the HR of cg11668749 was maximal (0.0012–0.15).
FIGURE 4.
FIGURE 4
[86]Open in a new tab
Univariate and multivariable Cox analyses. (A) Cox univariate analyses,
19 methylation sites were selected by p < 0.05. (B) Cox multivariate
analyses
3.4. Building a predictive DNA methylation site signature
To investigate the performance of the five methylation sites in
predicting recurrence, we calculated the RS of the five‐site signature
of all the patients in the training group using the RS formula as
follows:
[MATH: RS=(‐1.54×methylat
ion level of cg26428455)+(2.00×methylation level
of cg13754259)+(1.48×methylation level
of cg17421709)+(1.48×methylation level
of cg19229344)+(‐4.33×methylat
ion level of cg11668749)
:MATH]
Using the median RS as the cutoff point, the patients in the training
group were divided into either a high‐risk group or low‐risk group. As
the median of RS in the training group was 1.11, the patients with
RS > 1.11, were assigned to the high‐risk group and the others with
RS ≤ 1.11 were assigned to the low‐risk group. The survival status and
distribution of the training group are shown in Figure [87]5A. The
results of Kaplan–Meier survival analysis indicated that the patients
in the low‐risk group were correlated with a higher survival rate than
those in the high‐risk group (p < 0.001). The prognostic RS of 171
patients was simultaneously calculated in the test group to confirm the
predicted results of the training group. Following the same logic,
these patients were also divided into the high‐ and low‐risk groups
based on the RS median from the training group. The survival rate of
patients in the low‐risk group was also significantly higher than that
of patients in the high‐risk group (Figure [88]5B), which was in
accordance with the findings from the training group. The RS
distribution was identical between patients in the training and test
groups (Figure [89]5C,D).
FIGURE 5.
FIGURE 5
[90]Open in a new tab
Kaplan–Meier survival analysis and RS distribution. (A) The
Kaplan–Meier survival analysis of OS for TCGA training group (n= 341).
(B) The Kaplan–Meier survival analysis of OS for TCGA test group
(n = 171). (C) Five methylation sites RS distribution in training group
(n = 341). (D) Five methylation sites RS distribution in test group
(n = 171). OS, overall survival; RS, riskscore; TCGA, The Cancer Genome
Atlas
3.5. Building a predictive nomogram based on the five‐methylation site
signature
To investigate the prognostic value of the five‐methylation site
signature, a separate ROC analysis was performed in the training and
test groups because a larger AUC of ROC offers a better model for
prediction. The results showed that the AUC was 0.70 in training group,
indicating that the signature has a good ability to predict the
survival of patients with HNSCC (Figure [91]6A). The signature was then
confirmed in the test group (AUC = 0.73), indicating that this
signature potentially represents a robust prognostic biomarker for
HNSCC (Figure [92]6B). We established a nomogram for OS prediction in
HNSCC based on the five‐methylation site signature. Independent
prognostic predictors, including cg26428455, cg13754259, cg17421709,
cg19229344, and cg11668749, were integrated into a nomogram, where the
probability of 1‐, 2‐, and 3‐year survival of patients with HNSCC was
calculated using the formula of Exp * Coef for each site
(Figure [93]6C). “Exp” refers to the degree of DNA methylation, and
“Coef” represents the corresponding Cox regression coefficient. The
result of the concordance index based on the multivariate analysis of
the five‐methylation site signature (0.66) clearly showed that the
nomogram had adequate prognostic power to predict the OS of patients
with HNSCC.
FIGURE 6.
FIGURE 6
[94]Open in a new tab
Build a predictive nomogram model of five methylation sites signature
for 1‐, 2‐, and 3‐year OS of HNSCC. (A) ROC curve for the 5‐year
survival prediction in training group. (B) ROC curve for the 5‐year
survival prediction in test group. (C) Nomogram for predicting 1‐, 2‐,
and 3‐year OS of HNSCC using five methylation sites. Each patient in
training group was assigned to a score on the point scale. By summing
up the total points of the five methylation sites lie on “Total Points”
axis, we could predict the probability of 1‐year and 3‐year OS rate
plotted on the three axes below. HNSCC, head and neck squamous cell
carcinoma; OS, overall survival; ROC, receiver operating characteristic
3.6. Building a nomogram consisting of clinical data and RS
A multivariate Cox regression analysis was performed in a cohort of 289
patients, in which age, gender, grade, and stage were set as the
co‐variables, to determine whether the prognostic value of RS was an
independent variable among the other clinical inputs. The multivariate
analysis showed that the RS (HR = 1.56, 95% confidence
interval = 1.29–1.9, p < 0.001) was independently associated with the
OS of patients with HNSCC in the multivariate analyses (Figure [95]7A).
A prognostic nomogram based on the multivariate analysis results was
formulated to establish an effective method to predict the
probabilities of 1‐, 2‐, and 3‐year OS in HNSCC, with a concordance
index of 0.65. A predictive nomogram was also established in which the
score integrated the other four independent prognostic variables
including age, gender, grade, and stage (Figure [96]7B), and each
variable received a point corresponding to the point axis upward. Then,
the 1‐, 2‐, and 3‐year survival rates were estimated by the total
points downward. Patients with complete clinical information would
obtain total points reflecting the probability of 1‐, 2‐, and 3‐year
survival. The prognostic nomogram showed that the RS provides a better
guiding value for clinical prognosis of HNSCC followed by stage.
FIGURE 7.
FIGURE 7
[97]Open in a new tab
Establishment of a nomogram for OS prediction in HNSCC. (A)
Multivariate analysis revealed a significant association between the RS
and OS (HR = 1.56, p < 0.001). (B) A nomogram combining clinical data
(age, gender, grade, and stage) with RS provides great guiding value
for predicting 1‐, 2‐, and 3‐year OS of HNSCC. HNSCC, head and neck
squamous cell carcinoma; HR, hazard ratio; OS, overall survival; RS,
riskscore
4. DISCUSSION
Head and neck squamous cell carcinoma is a broad category of carcinomas
arising in the nasal cavity, oral cavity, pharynx, larynx, thyroid, and
esophagus.[98]^25 As HNSCC is often diagnosed at a very advanced stage,
more sensitive prognostic approaches are urgently needed. Currently,
the traditional prognostic determinant is based on the TNM staging
system; however, its clinical outcome differs greatly among patients,
even at the same stage. Therefore, the TNM staging system is not
sufficient for personalized treatment because of the unpredictable
clinical outcomes among patients.[99]^12 Over the past few years,
several powerful predictive biomarkers have been reported to improve
the clinical management of HNSCC. DNA methylation is a well‐known
epigenetic modification that alters the expression of key
tumorigenesis‐associated genes without any alteration in the genetic
sequence.[100]^26 These modifications have been shown to be closely
related to the occurrence and development of cancers, and many related
biomarkers have been reported.[101]^27, [102]^28 Tumor‐specific
methylated sites are critical for the accurate diagnosis of
cancers.[103]^29 As aberrant DNA methylation has been shown to be
associated with HNSCC tumorigenesis, it may serve as novel predictive
biomarkers for the prognosis in HNSCC.[104]^30, [105]^31 DNA
methylation genes, including p16, p15, p14, DAPK, and E‐cadherin,
associated with HNSCC tumorigenesis have been reported in previous
studies.[106]^32, [107]^33 Schröck et al. revealed that DNA methylation
of SHOX2 and SEPT9 is associated with HNSCC development through a
prospective cohort study.[108]^30 In this study, we found that DNA
methylation may be used for HNSCC diagnosis, staging, risk
stratification, and monitoring. Sailer et al. reported that DNA
methylation of PITX3 was an independent prognostic biomarker of OS
prediction in patients with HNSCC, and the methylation gene was used to
process the risk stratification for individualized treatment.[109]^31
These findings suggest that the evaluation of aberrant DNA methylation
genes may potentially serve as biomarkers for the diagnosis or
prognosis of HNSCC.
In this study, we collected and analyzed the methylated genes and
expression profiles of patients with HNSCC from the TCGA database and
identified 22 DMGs corresponding to 246 methylated sites identified
above. Through a series of statistical analyses, a RS signature
composed of five methylated sites (cg26428455, cg13754259, cg17421709,
cg19229344, and cg11668749) related to the prognosis of HNSCC was
verified. We then constructed a visualized nomogram model that combined
age, gender, grade, and TNM stage as covariates with the RS signature
to predict, the 1‐, 2‐, and 3‐year OS of patients with HNSCC. Ma et al.
developed a four‐gene methylation signature consisting of ZNF10,
TMPRSS12, ERGIC2, and RNF215. Their work demonstrated that this
signature could predict the survival outcomes of patients with HNSCC
and provided a potential therapeutic target for HNSCC therapy.[110]^17
Another study by Pan et al. screened six methylation‐driven genes
related to the prognosis of HNSCC, and constructed a linear risk model
with the six genes, they also revealed that all six genes may be used
as independent prognostic markers and represented potential drug
targets.[111]^34 Few studies have previously reported the prognostic
signatures based on methylation sites in HNSCC. Therefore, we conducted
the present study to investigate the prognostic value of methylated
sites in HNSCC. The prognostic nomogram model of HNSCC established in
our study comprehensively considered the clinical information of
patients, and its visualization characteristics were conducive to guide
clinical judgment regarding the prognosis of patients.
In this study, a RS signature was established using five methylation
sites that were mapped to four DMGs, including NEUROG3 (cg26428455),
ACTA1 (cg11668749), NKX2‐3 (cg19229344, cg13754259), and RP11‐1006G14.2
(cg17421709). Among these DMGs, NEUROG3, ACTA1, and NKX2‐3 were already
annotated in earlier studies, whereas RP11‐1006G14.2 was unknown.
NEUROG3 was found to be essential for endocrine differentiation in the
pancreatic endocrine lineage and shown to be the earliest marker
specific to pancreatic endocrine lineage.[112]^35 Recently, a
bioinformatic analysis based on the aberrantly methylated
differentially expressed genes and pathways in colorectal cancer (CRC)
showed that ACTA1 may serve as an aberrant methylation‐based biomarker
for precise diagnosis and treatment of CRC.[113]^36 NKX2‐3, another DMG
related to HNSCC is one of the most critical epigenetic markers
associated with the pathogenesis of human melanoma cell lines.[114]^37
NKX2‐3 was found to be upregulated in B cell lines and intestinal
tissues from patients with Crohn's disease and downregulated in
CRC.[115]^38, [116]^39, [117]^40 Among the four DMGs, only ACTA1 was
previously reported to be an early detectable marker in certain tumors
and a potential prognostic biomarker.[118]^41, [119]^42, [120]^43,
[121]^44, [122]^45 Yang et al. revealed that ACTA1 is crucial for
regulating the occurrence and progression of HNSCC, and represents a
potential target for individual clinical treatment.[123]^42 Our study
provides a context for further investigations into the functions of the
four DMGs.
The stage‐specific survival time of HPV + HNSCC is significantly longer
than that of HPV − HNSCC.[124]^46 HPV has been identified as a risk
factor for tumors in the oropharyngeal subsite.[125]^47 We reviewed the
HPV status of TCGA HNSCC dataset. Our data showed that the HPV‐negative
group had a higher RS and lower survival rate than the HPV‐positive
group (Figure [126]S1A,B). HNSCCs arising from different origins,
therefore, we compared the predictive value of the prognostic model in
HNSCCs of different origins between the two main subtypes of HNSCC
(larynx and tongue cancer) in the TCGA database. As shown in Figure
[127]S1C,D, the survival rate between the two subtypes was not
significantly different. However, we did not include tumor site and HPV
status as clinical covariates for the following reasons. First, 89
cases with HPV status accounted for a low proportion of the total
number of samples and consisted of different tumor sites (Table
[128]S1). Although the survival time was increased with HPV positivity
in pharyngeal and oropharyngeal cancer, the available sample types and
size do not provide tangible evidence for the other HNSCC subtypes.
Second, HPV detection is not a routine test for all HNSCC subtypes. If
HPV status were included, the nomogram would appear impractical. Tumor
site does not show a univariate relationship with outcome and is not
included in the multivariate Cox proportional hazards regression model.
Third, the four routine clinical variables were carefully chosen, given
the number of events available, to ensure parsimony of the final
nomogram.
5. CONCLUSIONS
In this study, five methylation sites (cg26428455, cg13754259,
cg17421709, cg19229344, and cg11668749) related to four DMGs (NEUROG3,
ACTA1, NKX2‐3, and RP11‐1006G14.2) were identified and used to develop
a prognostic model for HNSCC. Our study not only provides an approach
for predicting the OS rate of patients with HNSCC, but also a new
prospect for evaluating the clinical treatment outcomes of HNSCC.
Although the calculated RS based on the five‐methylation site signature
combined with the clinical data provides a valuable predictive model
for the prognosis of HNSCC, there are still some limitations in this
study. Obtaining large number of clinical samples, especially from the
nasopharyngeal site remains a challenge. It should be noted that the
potential mechanisms underlying the presence of prognostic methylation
sites in HNSCC remain unknown and further studies are needed to
investigate their functional roles in tumors. In addition, the accuracy
and robustness of the prognostic signature needs to be confirmed in
clinical practice.
CONFLICT OF INTERESTS
The authors declare that they have no competing interests.
AUTHOR CONTRIBUTIONS
Conceived and designed the experiments: Dayang Chen, Mengmeng Wang,
Ying Guo, Xiuming Zhang and Dan Xiong. Literature research and data
acquisition: Ying Guo, Wei Wu and Xiang Ji. Designed analysis
framework: Dayang Chen, Xiaowen Dou and Dan Xiong. Data analysis and
interpretation: Mengmeng Wang, Ying Guo, Huamei Tang and Xiuming Zhang.
Manuscript submission and revision: Zengyan Zong, Dayang Chen and
Mengmeng Wang. Manuscript writing: All authors. Final approval of
manuscript: All authors.
Supporting information
FIGURE S1
[129]Click here for additional data file.^ (15.4MB, tif)
Table S1
[130]Click here for additional data file.^ (17.2KB, docx)
ACKNOWLEDGMENTS