Methylated genomic loci encoding microRNA as a biomarker panel in tissue and saliva for head and neck squamous cell carcinoma
Cao et al. Clinical Epigenetics
Methylated genomic loci encoding microRNA as a biomarker panel in tissue and saliva for head and neck squamous cell carcinoma
Yu Cao 7 8
Katherine Green 8
Steve Quattlebaum 8
Ben Milam 8
Ling Lu 8
Dexiang Gao 6
Hui He 8 13
Ningning Li 8 12
Liwei Gao 8 11
Francis Hall 8
Matthew Whinery 8
Elyse Handley 8
Yi Ma 8 10
Tao Xu 15
Feng Jin 14
Jing Xiao 9
Minjie Wei 7
Derek Smith 6
Sophia Bornstein 0 4
Neil Gross 1 5
Dohun Pyeon 15
John Song 8
Shi-Long Lu 2 3 7 8
0 Department of Radiation Oncology, Cornell University , New York, NY , USA
1 Department of Head and Neck Surgery, MD Anderson Cancer Center , Houston, TX , USA
2 Department of Pathology, University of Colorado Anschutz Medical Campus , 12700 E19th Avenue, Aurora, CO 80045 , USA
3 Department of Dermatology, University of Colorado Anschutz Medical Campus , 12700 E19th Avenue, Aurora, CO 80045 , USA
4 Department of Radiation Oncology, Oregon Health & Science University , 3181 SW Sam Jackson Park Road, Portland, OR 97239 , USA
5 Department of Otolaryngology, Oregon Health & Science University , 3181 SW Sam Jackson Park Road, Portland, OR 97239 , USA
6 Department of Biostatistics, University of Colorado Anschutz Medical Campus , 12700 E19th Avenue, Aurora, CO 80045 , USA
7 Laboratory of Precision Oncology, China Medical University School of Pharmacy , No. 77 Puhe Road, Shenyang 110122 , China
8 Department of Otolaryngology, University of Colorado Anschutz Medical Campus , 12700 E19th Avenue, Aurora, CO 80045 , USA
9 Department of Oral Pathology, Dental School of Dalian Medical University , Dalian 116044 , China
10 Department of Otolaryngology, The First University Hospital of China Medical University , Shenyang 110001 , China
11 Department of Radiation Oncology, China Japan Friendship Hospital , Beijing , China
12 Department of Medical Oncology, Peking Union Medical School Hospital , Beijing 100730 , China
13 Research Laboratory and Department of Hematology, Benxi Central Hospital , Benxi 117000 , China
14 Department of Surgical Oncology, The First University Hospital of China Medical University , Shenyang 110001 , China
15 Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus , 12700 E19th Avenue, Aurora, CO 80045 , USA
Background: To identify aberrant promoter methylation of genomic loci encoding microRNA (mgmiR) in head and neck squamous cell carcinoma (HNSCC) and to evaluate a biomarker panel of mgmiRs to improve the diagnostic accuracy of HNSCC in tissues and saliva. Methods: Methylation of promoter regions of mgmiR candidates was initially screened using HNSCC and control cell lines and further selected using HNSCC and control tissues by quantitative methylation-specific PCR (qMS-PCR). We then examined a panel of seven mgmiRs for validation in an expanded cohort including 189 HNSCC and 92 non-HNSCC controls. Saliva from 86 pre-treatment HNSCC patients and 108 non-HNSCC controls was also examined using this panel of seven mgmiRs to assess the potentials of clinical utilization. Results: Among the 315 screened mgmiRs, 12 mgmiRs were significantly increased in HNSCC cell lines compared to control cell lines. Seven out of the 12 mgmiRs, i.e., mgmiR9-1, mgmiR124-1, mgmiR124-2, mgmiR124-3, mgmiR129-2, mgmiR137, and mgmiR148a, were further found to significantly increase in HNSCC tumor tissues compared to control tissues. Using multivariable logistic regression with dichotomized variables, a combination of the seven mgmiRs had sensitivity and specificity of 92.6 and 92.4% in tissues and 76.7 and 86.1% in saliva, respectively. Area under the receiver operating curve for this panel was 0.97 in tissue and 0.93 in saliva. This model was validated by independent bootstrap validation and random forest analysis. Conclusions: mgmiR biomarkers represent a novel and promising screening tool, and the seven-mgmiR panel is able to robustly detect HNSCC in both patient tissue and saliva.
Head and neck squamous cell carcinoma; microRNA; DNA methylation; Biomarker; Tissue; Saliva
Head and neck squamous cell carcinoma (HNSCC)
compromises approximately 90% of all head and neck cancers
and 5% of all malignancies [
]. HNSCC has also seen an
increasing rate of prevalence over the past 30 years due to
HPV infection . Despite advancements in cancer
therapy, the prognosis for HNSCC patients remains poor [
The low survival rate is in stark contrast to the increase in
survival rates of many other cancers. One of the main
reasons for the poor prognosis of HNSCC is that by the time
of diagnosis, more than half of HNSCC patients have
locoregionally advanced disease. Therefore, early detection
may be key to improving survival rates in the future [
Current early screening methods for HNSCC in the
clinic are limited to physical examination or optical
devices by either dentists or primary care physicians. These
then lead to a referral to a specialist. Around that time,
medical imaging, such as MRI, CT scan, or laryngoscopy,
are still the main methods for initial clinical diagnosis,
leading eventually to a biopsy or surgical procedure for
]. These initial imaging methods are
subjective, inaccurate, invasive, costly, and inconvenient.
Moreover, utilizing the current system leads to
significant diagnostic delays so that by the time of diagnosis,
over 2/3 of HNSCC patients already have in advanced
stage disease [
]. Thus, development of an objective,
accurate, non-invasive, low cost, and convenient method
for early detection would be highly beneficial.
Development of cancer-specific biomarkers for detection
of initial HNSCC and recurrence has been widely explored
using DNA-based (loss of heterozygosity, mutation, and
DNA methylation), RNA-based, and protein-based assays
in both patient tissues and saliva [
]. DNA methylation
usually occurs early in the process of tumorigenesis and
has been widely developed as a basis for biomarkers for
human cancers [
]. Several methylation markers have
been approved by the FDA for clinical application,
including MGMT in glioblastoma  and the ColoGuard
stoolbased screen for colorectal cancer patients [
Development of DNA methylation biomarkers for HNSCC
detection has also been reported [
]. However, biomarker
studies are still limited to the research phase, and no
biomarker-based assays for early detection have been used
clinically for HNSCC patients.
MicroRNAs (miRNAs) are a class of non-coding small
RNAs, which negatively regulate gene expression at the
post-transcriptional level [
]. A number of miRNAs
have been found to be deregulated in human cancers
through various mechanisms [
]. One of the
mechanisms responsible for reduced or loss of miRNA
expression is epigenetic silencing of miRNA genes by DNA
methylation at the genomic loci encoding the miRNAs
]. Currently, more than 20 miRNAs have been
reported to be silenced by DNA methylation in multiple
human cancers [
]. We have reported that DNA
methylation of miR9 specifically occurred in a subset of
human HNSCC tissue samples . However, their
potential as a novel class of biomarker for human cancer
detection has not been fully assessed.
We hypothesized that DNA methylation at the
genomic loci encoding miRNA (mgmiRs) represents a novel
class of modification and could be efficiently utilized for
human cancer including HNSCC. We tested this
hypothesis by screening and selecting mgmiRs in both cell
lines and tissues from HNSCC patients and normal
controls. We then investigated panel of seven mgmiRs, i.e.,
9-1, 124-1, 124-2, 124-3, 129-2, 137, and 148a, in an
expanded patient’s cohort, including 189 HNSCC tissues
and 92 control tissues. The translational application of
this panel of mgmiRs was further evaluated in saliva
from 86 HNSCC patients and 108 control patients.
Lastly, we assessed the association of individual mgmiRs
with demographic and clinical pathological information
in both tissue and saliva.
This study was conducted on human HNSCC surgical
samples from both the University of Colorado Anschutz
Medical Campus and the Oregon Health & Science
University (OHSU) under the Institutional Review Board
approval protocols from each institution. A written
informed consent was obtained from each subject. A total
of 281 different tissue specimens were used. Among them,
189 samples were HNSCC specimens from the time of
surgical resection and constituted our “tumor” group.
Ninety-two samples were from non-HNSCC patients
undergoing surgeries for sleep apnea or tonsillectomy with
no history of malignancy and used as our “non-HNSCC
control” group (Table 1). Saliva samples were collected in
86 previously untreated HNSCC patients and 108 control
patients including subjects enrolled in a community
screening study and non-HNSCC patients undergoing
surgeries for sleep apnea or tonsillectomy (Table 1).
Enrollment included collection of demographic information,
risk factor history, and clinical pathological information.
All information was registered in a Research Electronic
Data Capture (REDCap) database.
Preparation of samples
After harvesting, tissue was immediately taken to the
laboratory where it was frozen and stored in liquid
nitrogen until DNA extraction. For saliva collection, patients/
volunteers were required to refrain from eating,
drinking, chewing gums, and smoking 30 min before head. At
the time of saliva collection, patients gargled with
normal saline solution two times for around 20 s each time.
Patients/volunteers were then instructed to spit their
saliva into the collection tube for 5 min without
swallowing. Once collected, samples were immediately
frozen and stored at − 80 °C until ready for use.
DNA extraction and bisulfite conversion of genomic DNA
Tissue DNA was extracted from each tissue sample using
the DNeasy Blood & Tissue kit (QIAGEN, Hilden,
Germany), and saliva DNA was extracted using the
QiaAmp DNA mini kit (QIAGEN, Hilden, Germany)
following the manufacturer’s instructions. Quantity and
quality of the extracted genomic DNAs were measured
using the Nanovue spectrophotometer (GE Healthcare).
Bisulfite conversion of 1 μg genomic DNA was performed
as described in the EZ DNA Methylation-Gold kit (Zymo,
Irvine, CA, USA) to create a template for qMS-PCR. The
bisulfate-modified genomic DNA was resuspended in
100 μl of water and stored at − 80 °C.
Quantitative methylation-specific PCR
Bisulfite-treated DNA was then used as a template for
qMS-PCR, which was performed using the
methylationspecific primers. For the primer designs, genomic
sequence for each miRNAs including 1000 base upstream
were obtained from the UCSC genomic browser website.
The primers for methylation analysis were designed on
the basis of this sequence using MethPrimer software.
All primer sequences are available upon request. The
analysis was performed using quantitative
methylationspecific PCR (qMS-PCR).
For each individual marker, the qMS-PCR protocol
was optimized prior to running the samples, in order to
identify the proper annealing temperature and maximize
the results to obtain a typical sigmoid result curve.
Melting curves and gel were integrated to determine the
specificity of each marker. Variables were adjusted for the
temperature, number of cycles, and length of each cycle.
Each reaction was performed in a 20-μl PCR mixture
consisting of 2 μl of bisulfite-converted DNA, 5 nmol/L
of forward primer, 5 nmol/L of reverse primer, and 4 μl
SYBR-green supermix (Biorad, Hercules, CA, USA).
QMS-PCR was run in triplicate on the CFX connect™
real-time detection system (Biorad, Hercules, USA).
First, samples were denatured at 95 °C for 5 min, this
was followed by 40 cycles of 95 °C for 30 s, and lastly for
a given primer, samples were exposed to the optimized
annealing/extension temperature for 1 min.
Standardization was done by using UMSCC10A cells
and subjecting the cells to methylation in vitro with
excess Sss1 methyltransferase (New England Biolabs,
Ipswich, MA, USA) to generate a completely methylated
DNA, and serial dilutions of this DNA were used for
constructing the calibration curve for each plate. Water
and reaction mix were also included in each plate to
serve as negative controls and to ensure that there was
no contamination. For each sample within each marker,
a relative methylation level was calculated using the
difference in Ct values by the standard 2−ΔCt method in
which β-actin was used as an internal reference gene. A
Ct of 15 to 30 was considered a high methylation level,
and a Ct of 35, a low methylation level. A Ct value of
more than 40 was considered as undetectable.
An HPV signal was detected with amplification of L1
consensus sequence using primers GP5+/GP6+ as previously
]. Briefly, 20 ng of genomic DNA extracted from
tissue samples and cell lines was used for PCR amplification
by the forward primer GP5+
(5′-TTTGTTACTGTGGTAGATACTAC) and the reverse primer GP6+
(5′-GAAAAATAAACTGTAAATCATATTC). The final PCR products
were analyzed by 2% agarose gel electrophoresis and
ethidium bromide staining.
Descriptive statistics such as mean, standard deviation,
proportion, and percentage were used to summarize
demographic and clinicopathological characteristics of
study subjects. Chi-square or two-group T tests, as
appropriate, were used to compare HNSCC patients and
A univariate and combined analysis of the seven
selected mgmiRs was carried out using logistic regression.
The receiver operating curve (ROC) was computed for
each of the analyses. For each of the univariate analyses,
the Youden’s index (J) was used to determine the optimal
cutoff point to dichotomize a continuous mgmiR based
on the ROC plots. J can be formally defined as J =
Maximum (sensitivity + specificity − 1) on a ROC plot. The
cut-point that archives this maximum is referred to as the
optimal cut-point because it optimizes the biomarker’s
differentiating ability when equal weight is given to
sensitivity and specificity [
]. Additionally, to indicate diagnostic
accuracy of each mgmiR, sensitivity, specificity, positive
predictive values (PPVs), and negative predictive values
(NPVs) were provided. The same quantities were also
calculated for the combination of the seven mgmiRs.
The bootstrap method was used to validate the
performance of the combination of the seven mgmiRs internally,
where 2000 random samples with replacement were
generated with each sample containing the same number of
observations as the original dataset. The same combined
analysis of the seven selected mgmiRs was carried out on
each sample and the average AUC and its 95% confidence
interval (2.5 and 97.5 percentiles) across the 2000 samples
were reported. In addition, random forest, a
nonparametric classification approach was used to estimate the
prediction performance using the seven mgmiRs using the
CARET package in R (Kuhn M. 2016. R package version
6.0-73). Random forest analyses were conducted
separately for tissue and saliva samples. The dataset was split
into training and testing datasets, using a 2/3 to 1/3 split
and random sampling. The random forest models were
trained using 5000 trees and 1000 bootstrap resamples. In
addition, for each type of sample (i.e., tissue or saliva), it
was assessed whether the presence of demographic
information (i.e., age, gender, and smoking history) improved
prediction results. The random forest models were then
applied to the testing dataset, and the ROC and area
under the ROC (AUC) were determined. Youden’s index
was used to determine a cutoff point on the ROC, and
sensitivity and specificity were determined. The median
and its 95% confidence interval for the sensitivity and
specificity were estimated using 2000 bootstrap resamples
using the pROC package in R.
DNA methylation at genomic loci encoding microRNAs were identified in HNSCC cell lines
We utilized the following approaches to screen
candidate methylated genomic loci encoding miRNAs
(mgmiRs) in HNSCC: (1) The UCSC genome browser
was used to obtain 1 Kb genomic sequences [GRch38]
of 5′-UTRs of 315 primary (Pri-) miRNAs in the Homo
sapiens miRBase [from has-let-7a-1 to has-mir-499b in
miRbase, http://www.mirbase.org]. (2) The CpG island
prediction software (MethPrimer) was then utilized to
identify 26 genomic loci with CpG islands (defined as
island size > 200 bp, GC content > 50%,
observed/expectation > 0.6). (3) By designing methylation-specific
primers and running quantitative methylation-specific
PCR (qMS-PCR), we amplified methylation signals in 22
mgmiRs and found 12 mgmiRs that had increased
methylation in human HNSCC cell lines compared to
normal head and neck cell lines (Additional file 1:
Figures S1 and S2). As shown in Additional file 2: Table S1,
we examined relative methylation levels in 12 HNSCC
cell lines including cell lines derived from age (ranging
from 22 to 70 years), both male and female, human
papillomavirus (HPV)-positive and HPV-negative HNSCCs,
an HNSCC from Fanconi anemia patient, and different
anatomic sites of the head and neck region. Four head
and neck normal cell lines were included as controls.
HNSCC patients and control patients were recruited for this study
We included 189 tumor tissues from HNSCC patients
and 92 control tissues from non-HNSCC patients
undergoing surgeries for sleep apnea or tonsillectomy (Table 1).
Clinical and demographic variables were similar in cases
and controls with exception of age. Subjects with
HNSCC patients were older than controls (63.23 vs.
54.98 years). Primary tumor sites included oral
squamous cell carcinoma (OSCC), 95 cases; oropharynx SCC
(OPSCC), 44 cases; larynx SCC (LSCC), 48 cases; and
two cases without location information. Among the 66
HNSCC tissue samples tested for HPV status, there were
25 (37.9%) HPV-positive cases and 41 (62.1%)
HPVnegative cases. Pathologic stage at diagnosis was T1 in
46 cases, T2 in 49 cases, T3 in 41 cases, and T4 in 50
cases; and N0 in 78 cases, N1 in 37 cases, N2 in 66
cases, and N3 in five cases. Clinical staging was stage I
in 34 cases, II in 20 cases, III in 59 cases, and IV in 73
cases. There were three HNSCC cases without TNM
information (Table 1). We also included 86 saliva from
pre-treatment HNSCC patients and 108 from control
patients of either undergoing surgeries for sleep apnea
or tonsillectomy, or coming for community screening.
Similar to tissue samples, clinical and demographic
variables were similar in cases and controls with exception
of age. Subjects with HNSCC patients were older than
controls (61.07 vs. 53.77 years). Primary tumor sites
included OSCC, 42 cases; OPSCC, 28 cases; LSCC, 13
cases, and three cases without location information.
Pathologic stage at diagnosis was T1 in 20 cases, T2 in
26 cases, T3 in 11 cases, T4 in 21 cases, and eight cases
without information of tumor size; and N0 in 28 cases,
N1 in 6 cases, N2 in 40 cases, and N3 in two cases.
Clinical staging was stage I in 10 cases, II in 13 cases, III in
17 cases, and IV in 36 cases. There are ten HNSCC
cases without node and stage information (Table 1).
A panel of seven mgmiR biomarker was identified and validated in HNSCC patient tissues
To further identify mgmiRs which can distinguish
HNSCC from control patient samples, we first examined
the 12 mgmiRs in a small sample cohort including 30
HNSCC patients’ tissue samples, and 25 age and
gendermatched normal head and neck tissues from either
tonsillitis or sleep apnea patients. Based on the comparison
of relative methylation levels, seven mgmiRs, i.e., 9-1,
124-1, 124-2, 124-3, 129, 137, and 148a, showed
significant elevation in HNSCC tissue compared to
nonHNSCC control tissues and were selected for further
study (Additional file 1: Figure S3).
We then expanded examination of the seven mgmiRs to
additional 159 HNSCC and 67 non-HNSCC control cases.
Fig. 1 and Additional file 2: Table S2 show the relative
methylation levels of the seven mgmiRs in the total of 189
(30 + 159) HNSCC and 86 (25 + 67) non-HNSCC control
tissues. The total methylation level in the HNSCC group
was significantly higher (p < 0.0001) than that in the
control group for all the seven mgmiRs. All data was analyzed
using both dichotomized and continuous variables. ROC
curves for HNSCC detection were generated using either
dichotomized variables or continuous variables
(Additional file 1: Figure S4A for each mgmiR). As shown in
Table 2, the univariate assessment of HNSCC diagnosis
from each of the seven dichotomized mgmiR variables in
tissue had sensitivities and specificities ranging from 43.9
to 77.3% and 85.9 to 100%, respectively. However, the
combined assessment of the seven mgmiRs in tissue resulted in
a higher sensitivity and a specificity that was within the
range seen in the univariate assessment, 92.6 and 92.4%,
respectively. The combined assessment of the seven mgmiRs
yielded 92.5% accuracy, 96.2% PPV, and 85.9% NPV with
an area under curve (AUC) of 0.97 (Table 2). Since age
difference is a confounder factor, we also included age
together with the seven mgmiRs for the combined
assessment. As shown in Table 2, inclusion of age slightly
enhanced specificity but no significant changes with other
parameters. Internal bootstrap validation had AUC of 0.97
with a 95% CI of 0.95–0.99. The results using continuous
forms of the mgmiR variables as opposed to dichotomous
forms were similar with 93.6% sensitivity, 92.4% specificity,
and AUC of 0.98 (Additional file 2: Table S3).
MgmiRs were associated with HPV infection and could be detected in early cancer stage HNSCC tissues
We looked for association between either individual
mgmiR or in combination and clinical pathological
characteristics. For the mgmiR combination, we first
estimated the probability of a tissue being estimated
Fig. 1 Relative methylation levels for seven mgmiRs (9-1, 124-1, 124-2, 124-3, 129-2, 137, 148a) in tissues from 189 HNSCC patients and 92
controls. The quantity of methylated mgmiRs was expressed as fold changes from the methylated mgmiR to that from the reference
as positive using logistic regression with the seven
mgmiRs then assessed the association between a
characteristic and the estimated status (case or control) of
the tissue. In general, the associations between the
mgmiRs and location of HNSCC, smoking status,
tumor size, node status, and stage were stronger for
the combination of the seven mgmiRs compared to
their individual assessments (Table 3). Notably,
mgmiR124-2 and mgmiR129-2 were found to detect
significantly more HPV-positive than HPV-negative
HNSCC cases (88.0 vs. 65.9%, p = 0.04; 84.0 vs. 56.1%,
p = 0.02, respectively). There were no significant
associations between tumor size, stage, and percentage of
positive cases detected by either individual mgmiR or
the seven mgmiRs as a panel. However, most
importantly, 93.5% of T1 and 91.2% of stage I HNSCC can
be detected by the seven mgmiRs as a panel (Table 3).
Similarly, there were no significant associations
1The combined model includes all the seven mgmiRs
2The combined model includes all the seven mgmiRs and age
Table 3 Association between mgmiRs and clinicopathological characteristics in HNSCC tissues
mgmiR9-1 mgmiR124-1 mgmiR124-2 mgmiR124-3 mgmiR129-2 mgmiR137
%(n) %(n) %(n) %(n) %(n) %(n)
between node status (N0 vs. N+) with the exception
of mgmiR124-1, which detected more N0 cases than
N+ cases (79.5 vs. 65.7%, p = 0.04, Table 3).
The seven mgmiR biomarkers were validated in HNSCC patient saliva
We then examined the seven mgmiR biomarkers in
saliva from 86 HNSCC patients and 108 normal controls.
The relative methylation levels of the seven mgmiRs in
HNSCC and control saliva are shown in Fig. 2 and
Additional file 2: Table S4. The total methylation level in the
HNSCC group was significantly higher than the control
group across the seven mgmiRs. All data were analyzed
using both dichotomized and continuous variables. ROC
curves for HNSCC detection were generated using either
dichotomized variables or continuous variables for each
mgmiR (Additional file 1: Figure S4B). The sensitivity
and specificity using dichotomized variables for HNSCC
diagnosis from single mgmiR in saliva ranged from 19.8
to 72.1% and 74.1 to 97.2%, respectively, and from the
seven combined mgmiRs in saliva 76.7 and 86.1%,
respectively. The combined seven mgmiRs yielded 83.0%
accuracy, 83.5% PPV, and 82.6% NPV with AUC 0.93
(Table 4). Inclusion of age together with the seven mgmiRs
slightly increased specificity (Table 4). Internal bootstrap
validation had AUC of 0.93 with a 95% CI of 0.89–0.96.
The results using continuous variables were better to those
using dichotomized variables with 83.7% sensitivity, 95.4%
specificity, and AUC of 0.95 (Additional file 2: Table S5).
MgmiR was detected in the saliva of early HNSCC
Similar to the tissue sample results, the associations
between the mgmiRs and location of HNSCC, smoking
status, tumor size, node status, and stage were generally
stronger for the combination of the seven mgmiRs
compared to their individual assessments (Table 5). The
combination of seven mgmiRs can detect 71.4, 82.1, and
76.9% of OSCC, OPSCC, and LSCC, respectively.
1The combined model includes all the seven mgmiRs
2The combined model includes all the seven mgmiRs and age
However, there was no significant association between
tumor location and positive cases detected by mgmiR
either individually or in combination (Table 5).
Importantly, 85.0% of T1 HNSCC and 80.0% of stage I
HNSCCs could be detected by the panel of mgmiRs,
although there were no significant associations between
tumor size, node status, stage, and mgmiR-positive cases
detected by either individual mgmiR or the combination
Complementary prediction accuracy and performance assessment further validated the panel of mgmiR biomarkers in HNSCC
The mgmiR biomarkers were further evaluated for tissue
and saliva samples using random forest models. Similar
to the combined logistic regression model results
reported above, the random forest models included all
seven mgmiR biomarkers. In addition, demographic
information’s influence (age, gender, and smoking status)
on prediction performance was also assessed. Excluding
demographic information, the randomly selected tissue
training dataset had 186 subjects, 62 (33%) controls and
124 (67%) cancers, and the independent testing dataset
had 92 subjects, 30 (33%) controls and 62 (67%) cancers.
With the inclusion of demographic information, the
tissue training dataset had 170 subjects, 60 (35%) controls
and 110 (65%) cancers, and the testing dataset had 84
subjects, 30 (36%) controls and 54 (64%) cancers, due to
missing in demographic variables. Alternatively, no
demographic information was missing for the saliva
samples and therefore the training and testing datasets
were the same between models with and without
demographic information. The saliva training dataset had 130
subjects, 72 (55%) controls and 58 (45%) cancers, and
the testing dataset had 64 subjects, 36 (56%) controls
and 28 (44%) cancers. Model performance (i.e., AUC,
sensitivity, and specificity) using the random forest
approach was similar to the combined seven-mgmiR
logistic regression results for both the tissue and saliva
samples when demographic information was not
included (Table 6). The mean decrease in Gini index was
used to evaluate variable importance. The demographic
variables were found to be the least important variables
in both the tissue and saliva samples (Additional file 1:
Figures S5-S6). However, including demographic
information in the random forest models resulted in slightly
but not significantly higher AUCs, sensitivities, and
specificities (Table 6).
Early detection of cancers in high-risk populations has
increased patient survival rates, which is exemplified in
colonoscopy for colon cancer, pap smear for cervical cancer,
and mammogram for breast cancer [
]. Recently, the
FDA approved the use of a stool-DNA test for colon
cancer as a screening tool. This stool-DNA test, which can be
used ahead of colonoscopy, significantly improved
sensitivity and specificity of colon cancer detection, patient
compliance, and reduced costs [
]. Nearly all current
diagnostic methods for HNSCC in clinic rely on physical
examination, medical imaging, and endoscopy techniques.
Molecular tests for HNSCC have been widely explored [
]. However, their application in clinic has been limited
by their poor performance (i.e., low sensitivity and
specificity). In this study, we reported a high sensitivity and
specificity molecular test can be achieved by examining DNA
methylation on the genomic loci of microRNA (mgmiR).
This is the first study to evaluate the effectiveness of
mgmiR, a novel class of molecule, as a biomarker panel
for HNSCC diagnosis using tissue and saliva samples from
a large HNSCC population.
Our “mgmiR” technique combines several advantages
of different molecular test technologies: (1) It is a
genomic DNA-based technique. Analysis of RNA or protein
in saliva samples relies on the stability of the mRNA or
protein, which is always a challenge as saliva harbors
high levels of RNAse and proteases [
]. In contrast,
genomic DNA has a higher stability and is less
susceptible to be affected by storage and shipping than
RNAor protein-based technologies. (2) It is a
methylationbased technique. In contrast with examining expression
level of mRNA, miRNA itself , or protein, which can
be either positive (increased) or negative (decreased),
DNA methylation converts a negative signal (reduced or
loss of expression) into a positive signal, which can be
used for the detection of cancer-specific signal in a high
background of normal non-cancer cells. In contrast with
detecting mutations, which usually occur at multiple
sites in individual tumors. DNA methylation usually
occurs on the same region of a gene, such as the promoter,
which greatly simplifies design and interpretation of
screening tests. In addition, DNA methylation occurs at
the early stage of cancer development, providing the
opportunity for early detection of cancer [
]. It also has
tissue specificity, providing a foundation for detection of
tumor of unknown primary, which is exemplified in a
EPICUP clinical trial study [
]. (3) Instead of
detecting methylation of coding genes, mgmiR detects
genomic loci encoding for microRNA. Given the lower
numbers of miRNAs, compared to coding mRNAs,
mgmiR-based technique will compensate or at least
provide novel biomarkers for current DNA
methylationbased HNSCC detection. (4) It is a qPCR-based
technique which has been more widely accepted in clinical
laboratories. It is highly sensitive, simple, easier to
handle, and has a lower cost than mutation search using
next-generation sequencing (NGS).
While the development of “liquid biopsy” is almost
focused on the analysis of cell-free DNA and/or circulating
tumor cells in the blood, saliva, together with sputum,
urine, etc., constitutes major resources for molecular
testing using “body fluids.” Blood-based liquid biopsy
has been largely applied to patients with late-stage
cancers or serves as companion biomarkers for treatment.
The sensitivity and specificity may limit their
applicability for detection of early-stage cancers. For example,
saliva has been shown to be a more sensitive predictor
than blood for detection of HNSCC [
]. Using the
recently developed most sensitive NGS, only about 50% of
Univariate and multivariable logistic regression with dichotomized variables
colon cancer patients can be detected at stage I, in
comparison with ~ 90% of patients that can be detected at
stages II–IV [
]. Although we do not have data from
blood to directly compare with the data from saliva, the
sensitivity from saliva for stage I HNSCC patients in our
study is 80%, which is higher than the results from the
blood reported in the literature [
]. Thus, it is likely
that the saliva-based method is more sensitive and can
detect cancer earlier than blood-based method. In
addition, for screening purposes, the nature of
noninvasive, self-sampling, convenient, and low-cost of
saliva test make it a better screening tool particularly for
rural or community primary care settings [
While this report focuses on identification and
validation of a panel of mgmiRs as biomarkers for HNSCC
by rigorous statistical analysis in a large sample size, the
functions and mechanism of individual mgmiRs and
corresponding miRNA in HNSCC pathogenesis are not
included in this report. One interesting question is how
the miRNAs are regulated by their mgmiRs. We have
reported that miR9 expression is restored upon DNA
demethylation agent 5-aza-cytidine treatment [
similar results have been obtained for miR124, miR137,
miR129-2, and miR148a (manuscripts in preparation),
indicating DNA methylation on mgmiRs is a common
mechanism for silencing miRNA expression. Another
interesting mechanism is how each individual miRNA
functions in HNSCC pathogenesis. While detailed
functional analysis including identification of their
downstream target genes is beyond the scope of this report,
we published the tumor suppression role of miR9 on cell
] and are preparing separate
manuscripts on characterizing functional role of individual
miRNA and their target genes in HNSCC pathogenesis.
Our controls are comprised of a significant numbers of
tonsillitis patients; however, we have shown inflammation
does not affect the ability of mgmiRs in distinguishing
cancer and controls, suggesting this panel of mgmiRs is
cancer-specific. An interesting focus of study is why
mgmiRs are methylated in cancer but not in normal tissue.
We do not quite understand the underlying mechanism
yet, but reports on aberrant regulation of DNA
methytransferases (DNMTs) by either smoking or HPV could be
potential mechanisms [
]. We have not tested these
mgmiRs in other cancers. However, there are reports of
mgmi124-2 detection in cervical cancers . Interestingly,
both cervical and HNSCC are squamous cell carcinomas,
and HPV is the common etiological factor in both cervical
and HPV+ HNSCC patients. Whether this marker
represents a common marker for HPV-related squamous cell
carcinoma is a potential area of future investigation.
The association between mgmiR biomarker(s) and
clinical pathological data yield several interesting
findings: (1) Statistical analysis showed no significant
difference of positive cases detected by mgmiRs in
tumor size, node involvement, and tumor stage. More
importantly, T1 size tumors were detected in 93.5% of
tissues and 85.0% of saliva samples, N0 tumors were
detected in 94.9% of tissues and 75% of saliva samples, and
stage I HNSCC patients were detected in 91.2% of
tissues and 80.0% of saliva samples. These data clearly
demonstrate the clinical usefulness of using the mgmiR
panel for early detection of HNSCC. (2) Although the
positive percentages of LSCC detected by either mgmiR
or the seven mgmiRs as a panel were lower than OSCC
and OPSCC tumor tissues, it is not statistically
significant. However, mgmiR148a seems to be limited in its
ability to detect LSCC, and it may be possible to use the
remaining six mgmiRs for LSCC detection in saliva. One
question here is the age difference between the control
group and HNSCC group would affect methylation of
mgmiRs in general population, since aging has been
shown to affect both methylation and cancer
]. We thus include age as a confounder with
the seven mgmiRs and compare prediction performance
with and without age (Tables 2 and 4). We have also
performed a random forest model of the seven mgmiRs
together with age, gender, and smoking status (Table 6).
Age itself is shown to slightly contribute to the
prediction and is ranked lowest in comparison with the other
seven mgmiRs. Anyway, it would be ideal in the future
to include more aged-matched controls whenever it is
possible in clinical study.
While the incidence of HPV-negative HNSCC
gradually drops down due to cessation of smoking, the
incidence of HPV-positive HNSCC has increased more
than twofold in the past 20 years [
]. In our data,
smoking status does not affect the results of the assay.
However, the mgmiR124-2 and mgmiR129-2 detected
more HPV-positive HNSCC cases than HPV-negative
HNSCC cases. Unfortunately, we do not have HPV data
on non-HNSCC control patients at this moment, which
would help us to better distinguish if this association is
HPV-associated or HPV-cancer-associated, and this
warrants a future study. One of the problems of current
HPV virus detection for either HNSCC or cervical
cancer is that the high number of patients who are HPV
positive without having a disease. Detection of HPV
virus alone cannot discriminate truly oncogenic
infection from transient “bystander” infection.
Immunostaining of the surrogate marker p16 on tissue sections
frequently generates false-positive results [
similar to the triage strategy used in cervical cancer
], the detection of mgmiRs in saliva samples will be
useful to further separate high-risk oncogenic infectious
HPV-positive patients from low-risk “passenger” HPV
infection and have great promise as triage tool for
HPV-positive HNSCC diagnostics.
We have successfully developed a diagnostic panel using
mgmiRs markers that demonstrated superior sensitivity
and specificity in the detection of HNSCC in both
tissues and saliva. The high sensitivity in detection of T1,
N0, and stage I HNSCC of this panel of markers
suggests their values in the early detection of HNSCC.
Further assessment using pre-malignant lesions and
prospectively monitoring high-risk patients will measure
their usefulness in clinic. The quantitative nature makes
the panel of mgmiRs an ideal candidate to use in
surveillance of HNSCC patients, including treatment efficacy,
prognosis, and prediction of recurrence and metastasis.
Evaluation of these clinical applications using this panel
of mgmiR markers are undergoing.
Additional file 1: Figure S1. Flow chart of mgmiRs search for HNSCC.
Figure S2. Screening of mgmiR in HNSCC using HNSCC and control cell
lines. Relative methylation level of the mgmiRs examined by qMS-PCR in
12 HNSCC cell lines (HNSCC) and 4 head and neck control cell lines
(Normal). Red frames highlight mgmiRs with significance difference
between HNSCCs and normal (p < 0.05). Figure S3. Selection of mgmiR
in HNSCC using HNSCC and control tissues. Relative methylation level of
the mgmiRs examined by qMS-PCR in 30 HNSCC tissues (HNSCC) and 25
control tissues (Normal). Red frames highlight mgmiRs with significance
difference between HNSCCs and normal (p < 0.05). Figure S4. ROC
curves using continuous variables for HNSCC detection. (A). ROC curves
comparing the seven mgmiRs with the largest areas under the curve for
tissues. (B). ROC curves comparing the seven mgmiRs with the largest
areas under the curve for saliva. Figure S5. Variable importance plot
from the Random Forest analysis for tissue data including (A) or
excluding (B) demographic information. Figure S6. Variable importance
plot from the Random Forest analysis for saliva data including (A) or
excluding (B) demographic information. (ZIP 509 kb)
Additional file 2: Table S1. Relative methylation levels of mgmiRs in
human HNSCC cell lines and normal head and neck cell lines. Table S2.
Relative methylation level in tissues from 189 HNSCC patients and 92
normal controls. Table S3. Univariate and multivariable logistic regression
with continuous variables in tissues. Table S4. Relative methylation level
in saliva from 86 HNSCC patients and 108 normal controls. Table S5.
Univariate and multivariable logistic regression with continuous variables
in saliva. (DOCX 42 kb)
AUC: Area under the ROC; HNSCC: Head and neck squamous cell carcinoma;
HPV: Human papillomavirus; LSCC: Larynx SCC; mgmiR: Methylation of
genomic loci encoding microRNA; miRNA: MicroRNA; NPV: Negative
predictive value; OPSCC: Oropharynx SCC; OSCC: Oral squamous cell
carcinoma; PPV: Positive predictive value; qMS-PCR: Quantitative
methylationspecific PCR; ROC: Receiver operating curve
We thank all participating subjects for their kind cooperation in this study.
This work was supported by the Whedon Cancer Detection Foundation,
University of Colorado Cancer Center, Cancer League of Colorado, and the
University of Colorado Academic Enrichment Fund (to S. Lu), Dick Brown
Head and Neck Research Fund (to J Song), China Postdoctoral Science
Foundation 2017M621178 (to Y. Cao) and the National Institutes of Health
(R01 DE026125 to D. Pyeon). S.L.L is an investigator of THANC foundation.
All authors made substantial contributions to the conception and design
and to the acquisition, analysis, or interpretation of the data. LSL and JS
designed the study. YC, KG, SQ, BM, LL, HH, NL, LG, FH, MW, YM, FJ, JX, and
MW performed mgmiR experiments. TX and DP did the HPV test in clinical
samples. DG and DS did the biostatiscal analysis. EH, SB, and NG consented
patients and provided clinical samples. All authors read and approved the
Ethics approval and consent to participate
This study was conducted on human HNSCC samples from both the
University of Colorado Anschutz Medical Campus and the Oregon Health &
Science University (OHSU) under the Institutional Review Board approval
protocols from each institution. Consent was obtained from each participant.
Consent for publication
Written informed consent was obtained from all study participants according
to institutional guidelines.
SL Lu and J Song are listed as inventors on a PCT application “Biomarkers for
head and neck cancer and methods of their use.” The other authors declare
that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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