Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Toward a SERS Diagnostic Tool for Discrimination between
Cancerous and Normal Bladder Tissues via Analysis of the
Extracellular Fluid
Edvinas Zacharovas, Martynas Velička,*Gediminas Platkevičius, Albertas C
̌
ekauskas, Aru̅nas Z
̌
elvys,
Gediminas Niaura,*and Valdas S
̌
ablinskas
Cite This: ACS Omega 2022, 7, 10539−10549
Read Online
ACCESS Metrics & More Article Recommendations *
sıSupporting Information
ABSTRACT: Vibrational spectroscopy provides the possibility for
sensitive and precise detection of chemical changes in
biomolecules due to development of cancers. In this work, label-
free near-infrared surface enhanced Raman spectroscopy (SERS)
was applied for the differentiation between cancerous and normal
human bladder tissues via analysis of the extracellular fluid of the
tissue. Specific cancer-related SERS marker bands were identified
by using a 1064 nm excitation wavelength. The prominent spectral
marker band was found to be located near 1052 cm−1and was
assigned to the C−C, C−O, and C−N stretching vibrations of
lactic acid and/or cysteine molecules. The correct identification of
80% of samples is achieved with even limited data set and could be
further improved. The further development of such a detection
method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during
the surgery.
1. INTRODUCTION
Bladder cancer (BC) is the 11th most commonly diagnosed
cancer for both genders. Higher rates of age-standardized
incidence are observed in males comparing to females (9.0 and
2.2 per 100 000 person/years, respectively).
1
The 5-year
recurrence and progression rates depend on clinical and
pathological factors and vary from 31% to 78% and from 0.8%
to 45%, respectively.
2
Because of the high recurrence rate and
complexity of the invasive diagnostic procedures, bladder
cancer has the largest economic burden to treat per patient
over their lifetimes.
3
BC is commonly diagnosed by white light
cystoscopy (WLC), followed by a bladder tissue biopsy.
Although, WLC is widely used, it has limitations on detecting
small malignant tumors, particularly sessile carcinoma in situ
(CIS). Photodynamic diagnosis (PDD), with “blue”light after
addition of 5-aminolaevulinic acid (ALA) or hexaminolaevu-
linic acid (HAL), has higher rates of sensitivity than WLC
(92% vs 71%). However, its specificity is significantly lower
than that of WLC (63% vs 81%), as false-positive results may
be easily produced by bladder inflammation (cystitis), due to
similar macroscopical appearances in some cases.
4
Samples of
bladder tissue biopsy are examined by pathologists, and
diagnosis is given according to BC histological criteria. The
most common histological type of BC is urothelial carcinoma
(UC), with approximately 90% of all cases. The remaining
∼10% of cases consist of squamous cell carcinoma,
adenocarcinoma, and small cell carcinoma.
5
Since 2004,
when new pathological grading system was introduced, low-
grade (LG) and high-grade (HG) categories were imple-
mented to define tumor differentiation. High-grade tumors are
less differentiated and encompass all G3 and part of the G2
entities from the previous classification.
6
Although, to date,
WLC is a good standard of BC diagnosis, it has a factor of
subjectivity during both endoscopy and the pathological
examination of the biopsy sample. It also requires repeated
invasive procedures and has a high expense rate per patient.
Therefore, there is need for a new accurate, noninvasive, and
low-cost diagnostic method. Recently, new magnetic resonance
imaging possibilities have been described with a completely
new standardized reporting system (VI-RADS).
7,8
While, it
may have improved the patients care through imaging of the
bladder with a better resolution of the tissue planes, there is
still need to perform an invasive procedure to have a sample
for pathological examination.
Received: January 4, 2022
Accepted: March 3, 2022
Published: March 17, 2022
Article
http://pubs.acs.org/journal/acsodf
© 2022 The Authors. Published by
American Chemical Society 10539
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
Sensitive and precise detection of chemical changes in
biomolecules due to development of cancers is possible using
vibrational spectroscopy methods, namely infrared (IR)
absorption,
9
Raman spectroscopy,
10−12
and surface-enhanced
Raman scattering (SERS) spectroscopy.
13−16
It has been
previously shown that both of these methods can be used to
discriminate between the cancerous and normal tissues or cells
of various cancers, like brain,
17−20
breast,
21−24
or others.
25−28
Similarly, we have showed that SERS and attenuated total
reflectance Fourier-transform infrared (ATR-FTIR) methods
can be used to detect kidney cancer through the analysis of the
extracellular fluid.
29,30
Nowadays, a lot of research has been
focused to nondirect cancer detection through “liquid biopsies”
since this method can be potentially noninvasive.
31−33
A
number of vibrational spectroscopy studies have been
performed regarding bladder cancer as well.
34−41
It has been
shown that FTIR spectroscopy can be used not only to
distinguish cancerous and normal bladder tissues
34,35
but also
to detect bladder cancer from bladder washings.
36
Raman
spectroscopy possesses several advantages comparing with
other spectroscopy methods, such as (i) negligible interference
from water, (ii) rich vibrational information on bonding and
interactions of molecular groups, (iii) narrow vibrational
bands, and (iv) resonance and surface enhancement
possibilities. The SERS technique overcomes the low inherent
sensitivity of ordinary Raman spectroscopy. Several groups
have demonstrated the promising advantages of the SERS
approach in analysis of bladder cancer.
37−52
The first attempts
to employ SERS spectroscopy for the analysis of the cultured
bladder cancer cells are described by Jin et al.
37
in 2015.
Following studies have shown that in vivo imaging of the
bladder tissue can be performed using SERS nanotags
38
or that
the noninvasive and muscle-invasive bladder cancer cells can
be determined from liquid biopsies.
39
Importance of develop-
ment of new highly effective SERS substrates for analysis of
bladder cancer cells was well-recognized.
40,42
Thus, highly
ordered silver nanopore and nanocap arrays were fabricated by
using porous layers of anodic alumina membranes.
42
Recently,
Chuang et al.
40
demonstrated the advantages of hollow
AuxCu1−xalloy nanoshells for SERS detection of bladder
cancer cells. Progress in SERS-based discrimination of various
cancer cell types including bladder cancer cells was achieved by
combining antibody-conjugated magnetic beads and antigen-
targeting SERS nanotags.
45
To improve the detection
precision, an internal-reference based ratio-metric SERS assay
method
41
and dual-mode Au-nanoprobe technique based on
determination of telomerase activity in cell extracts and urine
of patients by combining SERS and calorimetry measure-
ments
46
were developed. The potential of SERS spectroscopy
for discrimination of high-grade and low-grade bladder cancer
cells was demonstrated.
47−49
The possibility to predict the
bladder cancer grade by SERS analysis of urine supernatant
and sediment was proposed.
47
Recently, a new elegant NIR-
SERS platform based on modified Au−Ag nanohollows was
developed for effective discrimination of high-grade and low-
grade bladder cancer cells.
48
In this work, we demonstrated that a label-free SERS
spectroscopy approach, in comparison with other approaches,
can be applied much more easily while still granting sensitive
chemical analysis. The analysis of extracellular fluid of bladder
tissue and the tissue itself was performed. We have employed
near-infrared (1064 nm) excitation wavelength ensuring
nonresonant and fluorescence-free SERS spectra of biosamples.
It was demonstrated that combination of near-infrared
excitation and citrate-reduced Ag nanoparticles as a substrates
increases repeatability of SERS spectra of biofluids.
53
If a
precise label-free SERS method would be developed it could
be further improved to benefit the clinical diagnosis. By
employing optical fiber probes SERS method is already being
proposed as a sensitive method allowing on site analysis.
54
Thus, if coupled with endoscopic analysis, fiber probes covered
with SERS active nanoparticles could be used to enhance the
sensitivity and minimize the invasiveness of the tumor
detection.
2. MATERIALS AND METHODS
2.1. Sample Collection. Spectral studies of the bladder
tissues were approved by the Vilnius Regional Biomedical
Research Ethics Committee (Document No. 2019/12-1178-
665). The samples of the bladder tissues for SERS
spectroscopic studies were obtained between December 2019
and March 2020 in the Urology Center of the Vilnius
University Hospital Santaros Clinics when performing
transurethral resection of urinary bladder (TURB) or radical
cystectomy (RC). Patients were eligible if they had a clinical or
radiological suspicion of bladder cancer and they required any
of the procedures mentioned (TURB, RC) according to the
latestguidelinesforbladdercanceroftheEuropean
Association of Urology. All patients gave an informed consent.
Ineligibility criteria were refusal to participate in the study,
positive urine culture, and untreated coagulopathy.
At the beginning of the TURB procedure, before cutting the
tumor, a single sample of healthy-looking bladder tissue was
obtained for the spectral studies. After the TURB procedure, a
single sample of cancer-suspicious tissue was obtained for the
spectral studies. Malignancy was confirmed pathologically by
examining the remaining resected tissue. When performing
RC, samples of healthy and malignant tissue were obtained
after the procedure. Immediately after surgery, bladder tissue
samples were submitted for histological and spectroscopic
analysis. Thus, every set of samples gathered for the
spectroscopic analysis contained the cancerous or cystitis-
affected tissue and the healthy-looking tissue, which was
collected from the bladder of the same patient. The latter
tissue type for the purpose of convenience is called normal
tissue in the manuscript. The true nature of the tissue that was
considered as healthy-looking was proved by the results of
histological examination.
2.2. Sample Preparation. Samples for the SERS analysis
of the whole tissue were prepared as follows. The small part of
the bulk tissue was cut with a clean scalpel blade. The resected
bladder cancer tissues were rather small due to the limitations
of the surgery (the resection procedure). Therefore, on
average, the analyzed tissue samples were around 1.5−2mm
in diameter. Subsequently, the cut of tissue was placed with the
cut side up, and a small amount of the concentrated colloidal
solution was deposited on top and dried. The SERS spectra
were then collected directly from the surface of the tissue.
Samples of extracellular fluid of the normal, cystitis-affected,
and cancerous bladder tissues analyzed in this work were
prepared in the following manner. A small part of the bladder
tissue was sliced offthe bulk tissue and smeared (pressing the
cut side) across the aluminum substrate, which was precleaned
with methanol. The formed extracellular fluid layer was dried
in an open environment at room temperature and used for
further studies. Such a sample preparation procedure results in
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10540
creation of a thin film of extracellular fluid, which also includes
single cells of the tissue. Since the samples of extracellular fluid
were taken by stamping of the tissue under study on glass
substrate, the stamp retains information about the morphology
of the tissue. The cancerous areas of the tissue are located in
the same places in the stamp, just with a much lower
concentration of the cells. Before collecting the SERS spectra, a
drop of colloidal solution was put on the top of dried
extracellular fluid film.
To ensure reproducibility of the SERS data, the same
procedure for the preparation of the samples and the colloidal
solution was reproduced in a very thorough and careful
manner in order to keep the experimental parameters always
the same (or at least as similar as possible). Therefore, high
reproducibility of the SERS spectra was achieved in this study.
The volume of the colloidal solution added on top of the
extracellular fluid or tissue sample for each measurement was
always the same, 10 μL. Therefore, the incubation time of the
AgNPs and the tissue or extracellular fluid layer samples was
relatively constant. The samples were measured immediately
after the nanoparticle solution drop was dried. Since small
drops of liquid were used, the drying process took around 30 s.
2.3. Measurement Equipment. The UV−vis electronic
absorption spectra of silver nanoparticles were recorded using
a two-channel UV−vis−NIR spectrophotometer Lambda 1050
(PerkinElmer, USA) equipped with two light sources,
deuterium and halogen lamps. Spectra were collected in a
wavelength range of 250 to 1100 nm and a resolution of 5 nm
were selected.
The Raman scattering and the SERS spectra were collected
using Fourier Transform (FT-Raman) MultiRAM spectrom-
eter (Bruker GmbH, Germany). The samples were irradiated
using 1064 nm wavelength Nd:YAG laser. Spectra collection
was done in 180-deg geometry. Gold plated hyperbolic 90-deg
angle mirror objective coupled with a CCD camera was used.
The focal length of the objective was 33 mm and the diameter
of the focused laser beam was 100 μm (an average intensity at
the sample of 955 W/cm2at 100 mW of laser power). A liquid
nitrogen cooled Ge detector was used to collect the scattered
light. All spectra were collected in the wavenumber range of
100−3600 cm−1with a resolution of 4 cm−1. A Blackman−
Harris 3 term apodization function and a zero-filling factor of 2
were used for the Fourier transform. To avoid time-dependent
changes in the biosample and increase repeatability of the
measurements, the time required to prepare the sample with
Ag nanoparticles and acquire the SERS spectrum was
sufficiently short, no longer than 5 min.
The variability of the experimental and SERS spectra was
calculated as follows. First, using the normalized experimental
spectra, the averaged SERS spectrum was calculated for each
class: cancer, normal, and cystitis. Second, using the spectral
data, the standard deviation was calculated for each individual
data point. Finally, the standard deviation was visualized
together with the averaged spectra for each class.
2.4. Preparation and Characterization of the Colloi-
dal Solution of Silver Nanoparticles. Silver nanoparticles
were prepared in accordance to the procedure described by
Lee and Meisel.
55
In short, 18 mg of silver nitrate (AgNO3,
99%, Merck, Germany) was dissolved in 100 mL of distilled
water. Next, an aqueous solution of AgNO3was heated to
boiling temperature while stirring constantly. When the boiling
point was reached, 2 mL of a 1% solution of trisodium citrate
(Na3C6H5O7, 99%, Merck, Germany) was added, and the
whole mixture was left heated for an additional hour while
being stirred rapidly. After 1 h, the solution was cooled to a
room temperature in an ice-bath. The synthesis procedure
results in a grayish-green solution of silver nanoparticles. To
increase the concentration of nanoparticles, the colloidal
solution was centrifuged for 10 min at 6500 rpm. After that,
15 mL out of the initial 30 mL solution was removed as a
supernatant. The left-over concentrated solution was used for
the Raman scattering measurements.
Biological media may affect the stability of synthesized Ag
nanoparticles.
56,57
Based on SERS measurements, we found
that the nanoparticles remained stable for more than 2 h. This
might be related to the fact that the samples of extracellular
fluid of bladder tissue and colloidal solution were dried. Also,
the whole process of sample preparation and SERS measure-
ment was rather short, no longer than 5 min. Recently, Valenti
and Giacomell
57
have demonstrated the stability of citrate-
capped Ag nanoparticles against dissolution in biologically
relevant conditions. The stability of silver nanoparticles capped
with different agents (including citrate), in various conditions
of biological media (different pH levels, electrolyte concen-
tration, buffers) was investigated by MacCuspie.
56
It was found
that the performance of the citrate-capped Ag nanoparticles
can be observed up to 5 h or more.
3. RESULTS AND DISCUSSION
3.1. Characterization of Silver Nanoparticles. Silver
nanoparticles were characterized by UV−vis spectroscopy and
transmission electron microscopy (TEM) analysis (Figure S1).
Only one broad band centered at 450 nm was observed in the
UV−vis spectrum. Integrated intensity of this band was found
to increase by a factor of 1.6 after centrifugation (6500 rpm)
indicating an increase in concentration of Ag nanoparticles.
Based on analysis of TEM image, the average diameter of
spherical nanoparticles was found to be about 80 nm. The size
distribution of synthesized Ag nanoparticles is shown in Figure
S2. The suitability of Ag nanoparticles for SERS studies was
tested by using uric acid as an adsorbate (Figure S3). The
calculated analytical enhancement factor for centrifuged at
6500 rpm nanoparticles was found to be 8 ×104.
Before conduction of the experiments with biological
samples, the SERS spectrum of the bare Ag nanoparticles
was recorded (Figure 1). Such a spectrum is needed in order to
eliminate the bands present from adsorbed stabilizing species
or impurities, which might seriously perturb the spectrum of
samples under investigation.
13,58
The strong feature near 236
cm−1dominates in the SERS spectrum. Similar intense band
(232 cm−1) was observed previously in SERS spectrum of
citrate-reduced Ag nanoparticles.
59
This band was assigned to
the stretching vibration of silver−oxygen bond. It should be
noted that stretching vibration of Ag−Cl bond occurs in the
similar wavenumber region.
60
A small amount from chloride
salt impurities in chemical compounds used for preparation of
Ag nanoparticles may contribute to the observed low-
frequency band. In this work, chloride salts were not used
for synthesis of Ag nanoparticles. Therefore, the stretching
vibration of Ag−O bond was suggested as a major contribution
for the low-frequency band at 236 cm−1observed in this work.
Thus, the low frequency spectral region is not useful for
analysis of biological samples; however, the frequency region
above 300 cm−1does not contain any distinct spectral features
and was explored for further SERS analysis.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10541
3.2. Comparison of Raman and SERS spectra. Figure 2
compares Raman and SERS spectra of normal and cancerous
bladder tissues and their extracellular fluid. One can see that
ordinary Raman spectra differ considerably comparing with the
SERS spectra from the same samples. Raman spectra from
tissue samples exhibit strong bands related to CH2deformation
vibration near 1438 cm−1, the Amide-I stretching mode at
1660 cm−1, and broad features in the range 1250−1350 cm−1
mainly due to the Amide-III vibrational mode.
61
In contrast,
SERS spectra of tissue exhibit many intense bands in the lower
frequency region. This is because SERS spectrum represent
adsorbed species at the surface of Ag nanoparticles and
operation of special surface selection rules.
58,62
In the case of
extracellular fluids, no ordinary Raman spectra are observed;
however, intense SERS spectra are acquired. As can be
observed, conventional Raman scattering spectra do not
provide clear information on the nature of the sample (normal
or cancerous). Slight spectral differences can be observed in
the Raman spectra of the tissue but these differences are
extremely small, and any discrimination of the tissues would be
complicated. Compared to that, spectral differences between
normal and cancerous samples observed in the SERS spectra
are much greater (especially in the case of extracellular fluid
samples). Because of the intense spectra from extracellular
fluids and strong response in the fingerprint spectral region of
tissue samples, in the following we will discuss only the SERS
studies.
3.3. SERS Spectroscopy of Bladder Tissue Samples.
The total of 58 bladder tissue samples of 30 different patients
(28 healthy, 25 cancer patients, and 5 patients affected by
cystitis) were collected and studied in this work. The
histological examination was performed for all the collected
samples. The results of histological analysis of the bladder
tissues used in this study are presented in Table 1. Cancers
were classified by TNM (tumor node metastasis), a globally
recognized standard for classification the extent of spread of
cancer.
For determination of the spectral differences between
healthy, cancerous, and cystitis-affected bladder tissues, SERS
spectra were recorded. The spectra were collected at five
different points for each of the bladder samples (tissues and
their extracellular fluid layers) in order to take into account
possible differences of the spectra at different measuring
points. The averaged SERS spectra of bladder tissues are
presented in Figure 3. The spectra were normalized by
Figure 1. SERS spectrum of the centrifuged colloidal solution of silver
nanoparticles used in this study. The excitation wavelength is 1064
nm.
Figure 2. Comparison of conventional Raman and SERS spectra of
normal (lower spectra, blue curve) and cancerous (upper spectra, red
curve) bladder tissues and their extracellular fluids. Values of Raman
shifts over broken bars denote the bands related with the strongest
spectral changes. The excitation wavelength is 1064 nm.
Table 1. Results of Histological Analysis of Bladder Tissues
Used in the Study
histological type TNM
a
differentiation grade number of patients
urothelial carcinoma pTa low-grade 9
urothelial carcinoma pTa high-grade 8
urothelial carcinoma pT1 high-grade 3
urothelial carcinoma pT2a high-grade 2
urothelial carcinoma pT2b high-grade 1
urothelial carcinoma pT3a high-grade 2
nonspecific cystitis −− 5
a
Abbreviation: TNM, tumor node metastasis.
Figure 3. Averaged SERS spectra of cystitis-affected, cancerous and
normal bladder tissue samples and the difference spectrum with a
magnified intensity (×4) between the averaged spectra of cancerous
and normal tissues. The gray areas in the spectra represent the
standard deviation of the intensity. The excitation wavelength is 1064
nm.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10542
applying vector normalization and were shifted along the SERS
intensity axis for clarity. Gray areas indicate the standard
deviation of the intensity of the SERS spectral bands. These
changes may be reasoned by the concentration variations due
to nonuniform distribution of structural molecules. The
distribution of cancer relevant molecules in the human body
depends on physiology, lifestyle, physical activity, food intake,
medication, illnesses, and other factors. Furthermore, in the
case of cancer, the concentration of such molecules in the
tissue may vary with different type of the tumor, its stage, and
morphological changes in the cancer cells. It is also important
to note that during the surgery the exact borderline between
normal and cancerous tissues is invisible. Therefore, cancerous
cells can be detected in a sample of a healthy tissue sample and
vice versa.
Analysis of the averaged SERS spectra of healthy and
cancerous bladder tissue revealed that no significant spectral
differences can be determined between them. To enhance
small spectral deviations between the studied samples, we have
constructed the difference spectrum; such an approach was
previously used for detailed Raman/SERS analysis of bladder
cancer.
11,47,50,51
The difference spectrum between the spectra
of cancerous and normal tissues showed two spectral bands of
interest (Figure 3). These are located at 660 and 891 cm−1.
Also, by comparison of these spectra with the SERS spectra of
tissues affected by cystitis, three SERS spectral bands that are
absent or have low intensity in the spectra of normal and
cancerous tissues were identified. These bands are located at
724, 1222, and 1438 cm−1. In order to explain the observed
differences in the SERS spectra of urinary bladder tissues, a
tentative assignment of the SERS bands was performed in
accordance with the literature.
13,44,61−73
It can be stated that
the main spectral differences may be related to the SERS
spectral bands of Amide III (1222 cm−1), adenine (724 cm−1),
cysteine (660 and 891 cm−1), and proteins (1438 cm−1). In
addition, the 1438 cm−1band may have some contribution
from oxygenated guanosine ring stretching vibrations.
72
It
should be noted that all of the spectral bands are directly
related to the constituents of the analyzed samples and not the
molecules in the composition of the colloidal solution itself.
No distinct spectral bands in the discussed spectral region were
observed in the SERS spectrum of the colloidal solution (see
Figure 1).
3.4. SERS Spectroscopy of Extracellular Fluid. The
sample smearing technique was chosen in this work, since it
was already used in our previous studies where we have shown
that extracellular fluid samples also contain single tissue
cells.
29,30
In these studies, we have tested the reproducibility of
such samples and have noticed that only minor changes in the
intensity of the spectral bands are observed. To average out the
small spectral differences which result from the small variation
of the sample composition or thickness, the SERS spectra of
extracellular fluid were also collected at five randomly chosen
points of every sample. The two different measurement points
were at least 1 mm apart. It should also be noted that the
diameter of the focused laser spot in our experiments was 100
μm what is a relatively large area if compared to Raman
microscopy measurements. Collection of the SERS signal from
such area could be regarded as an averaging of the spectral
information since in Raman microscopy the diameter of the
area of measurement is only few micrometers. To represent the
reproducibility of a typical sample, the SERS spectra collected
at five randomly selected points of the extracellular fluid of
normal bladder tissue is presented in the Figure 4. As can be
observed, only slight differences in intensity result from the
point of measurement.
An important matter is the spectral variance source. Figure 5
compares averaged intrasample and interpatient SERS spectra.
One can see that the intrasample variance is quite small in
comparison to the interpatient spectra. This shows that the
variance comes from the differences in the tissues of the
patients. Such differences are a result of diseases and their
progression in case of spectra of the cancerous or cystitis-
affected samples and most probably the different lifestyles
(food, metabolism, etc.) in the case of normal samples. The
SERS spectra of extracellular fluid of healthy, tumor, and
cystitis-affected tissues containing single cells are presented in
Figure 6. It is notable that the standard deviation of the
intensity of the vibrational bands (visualized by gray area) in
Figure 4. SERS spectra of extracellular fluid of normal bladder tissue
collected at five randomly selected points of the sample. The
excitation wavelength is 1064 nm.
Figure 5. Comparison of averaged intrasample and interpatient SERS
spectra of extracellular fluid of normal, cancer, and cystitis-affected
samples. The gray areas in the spectra represent the standard
deviation of the intensity. The excitation wavelength is 1064 nm. Five
experimental spectra were averaged.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10543
these spectra may be attributed not only to the individual
changes due to different lifestyle of each patient but also to the
peculiarities of the sample preparationthe uneven surface of
the extracellular fluid layers. This is because the intensity of the
conventional and surface enhanced Raman scattering signals
depends on the thickness of the test specimen (the number of
the molecules contributing to the Raman signal). A very thin
layer of an extracellular fluid with single cells results in the
sufficiently strong SERS signal, while the SERS spectrum of the
thick layer may resemble the Raman spectrum of the whole
tissue.
The main spectral differences that allow the discrimination
of the healthy and cancerous tissues can be observed in the
SERS spectra of extracellular fluid presented in Figure 6. These
vibrational spectral bands are located at 1052 and 1414 cm−1,
respectively. The SERS band of the extracellular fluid of
cancerous bladder tissue at 1052 cm−1is rather intense, while
the band at 1414 cm−1is less intense in the spectra of healthy
and cystitis-affected tissues. Some SERS spectra of healthy
tissue do not contain these bands at all.
Spectral alterations are more clearly visible in the SERS-
difference spectrum (Figure 6) constructed from averaged
cancerous and normal and cancerous and cystitis affected
bladder spectra. The positive-going features in this spectrum
are related with an additional or intensified bands characteristic
for cancerous samples. Thus, an intense and sharp positive-
going feature is visible at 1052 cm−1, while two lower intensity
bands appear near 1414 and 660 cm−1. It should be noted that
intensification of the latter band is not clearly visible from the
averaged SERS spectra of the extracellular fluid of the
cancerous bladder tissue. In order to highlight the spectral
differences and variability of the spectral marker bands, the
positions of the marker bands, the mean intensity, and the
standard deviation values observed in the SERS spectra of
bladder tissues (Figure 3) and extracellular fluids (Figure 6)
are listed in Table 2.
3.5. SERS Marker Bands of Bladder Cancer/Cystitis.
Let us define the origin of the bands, characteristic to
cancerous samples (Table 3). SERS spectra of selected
biomolecules which could contribute to the observed spectra
of bladder samples obtained at 1064 nm excitation wavelength
are presented in Figure 7. One can see that tyrosine residues
from proteins, cysteine, ATP, thymine ring, guanine ring, and
lactic acid may contribute to 1052 cm−1band. In the case of
Tyr and Thymine, this is a relatively low intensity feature
compared with other modes of these compounds. However, in
the case of lactic acid molecules, this is the dominant and
characteristic vibrational mode. Cysteine molecules may also
contribute to the observed spectra due to exhibition of broad
and intense feature near 660 cm−1associated to C−S
stretching vibration in addition to the 1052 cm−1band.
Thus, based on examination of presented SERS spectra of
selected compounds and literature data analysis we suggest
that the major contribution for the band located at 1052 cm−1
comes from the ν(C−O), ν(C−N), and ν(C−C) stretching
vibrations of lactic acid
44,60−69
and/or cysteine
70
molecules.
The increased intensity of this band in the cancerous tissue can
be also explained by the increased amount of the cysteine,
since such compound is related to the development of cancer.
More precisely, a correlation between the cancer growth and
the availability of cysteine was shown to be especially strong in
bladder cancer.
74
Thus, the uptake of cysteine molecules is
seen to be increased in the cancerous tissue.
75
The vibrational band in the SERS spectra of the extracellular
fluid of the bladder cancer tissues, observed at 1414 cm−1, was
found to be associated with stretching vibrations of protein,
DNA, lipids, and lactic acid molecules (Figure 7). SERS-
difference spectrum suggests the presence of two positive-
going features in this spectral region at 1414 and 1448 cm−1
(Figure 6). Intense bands in this spectral region are
characteristic for lipid molecules due to scissoring bending
vibration of methylene groups.
67
Lipids perform the function
of cellular energy storage and are involved in signal
transduction, cell proliferation, and growth processes. Since
more energy is used during the uncontrolled division and
Figure 6. Averaged SERS spectra of the extracellular fluid of cystitis-
affected, cancerous, and normal bladder tissues and the difference
spectra with a magnified intensity (×4) between the averaged
extracellular fluid of cancerous and normal and cancerous and cystitis-
affected tissues. The gray areas in the spectra represent the standard
deviation of the intensity. The excitation wavelength is 1064 nm.
Table 2. Mean Values and the Standard Deviation of the Main Spectral Bands Observed in the SERS Spectra of Bladder Tissue
and Extracellular Fluid
sample wavenumber, cm−1INormal,au ICancer,au ICystitis,au
tissue 660 1.26 ±0.42 1.38 ±0.35 1.20 ±0.40
724 0.68 ±0.28 0.69 ±0.17 0.85 ±0.49
1222 0.62 ±0.20 0.64 ±0.14 0.92 ±0.27
1438 0.91 ±0.20 0.90 ±0.15 1.16 ±0.23
extracellular fluid 660 0.58 ±0.36 0.68 ±0.38 0.50 ±0.13
1052 0.37 ±0.17 0.89 ±0.39 0.39 ±0.16
1414 0.86 ±0.10 0.99 ±0.26 0.83 ±0.11
1448 1.08 ±0.07 1.20 ±0.14 1.20 ±0.09
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10544
growth of cancer cells, lipid metabolism is disrupted in tumor
cells.
76
The altered metabolism of these molecules may lead to
different lipid concentrations in healthy and cancer cells. These
changes depend on the type and stage of the cancer and its
aggression. Changes in the intensity of these bands can be
influenced by changes in the body’s genetic material, which is
typical for the cancer. It is known that structural mutations in
DNA can be one of the causes of the formation of cancer cells.
The increase of lactic acid concentration in cancerous tissue
canbeexplainedbytheWarburgeffect.
77
Adenosine
triphosphate (ATP) molecules are the major source of energy
in the cell. In healthy intact cells, most ATP is synthesized by
oxidative phosphorylation in the presence of ADP (adenosine
diphosphate) and phosphoric acid during oxidation−reduction
reactions. However, cancer cells are characterized by a rapid
process of glycolysis, the breakdown of glucose molecules into
pyruvate molecules, and the formation of ATP molecules.
Glycolysis (the anaerobic pathway for glucose metabolism), in
terms of ATP synthesis, is not as efficient as oxidative
phosphorylation. However, it produces metabolites that are
useful for cell division and tumor growth, including lactic acid
secreted during pyruvate fermentation.
78
Partial oxidation of
DNA is also related with genomic mutations and development
of cancer.
79
Kundu and Loppnow recently demonstrated that
major oxidative damage of DNA associated with 8-oxodeox-
yguanosine (8-oxo-dG) molecules can be reliably detected by
ultraviolet resonance Raman spectroscopy.
72
The Raman
marker band of 8-oxo-dG ring stretching vibration was found
at 1449 cm−1, which is close to our observed 1414/1448 cm−1
SERS bands.
The vibrational band observed in the SERS-difference
spectrum at 660 cm−1is most likely related to the breathing
vibration of guanine rings in DNA or C−S stretching vibration
of cysteine residues in proteins (Figure 7). This is again
supported by the already mentioned role of the cysteine
molecules in the proliferation of bladder cancer.
3.6. Principal Component Analysis (PCA) of the SERS
Spectra. To evaluate the reliability and accuracy of the
spectral features of the cystitis-affected bladder tissue, the
principal components analysis (PCA) was performed using an
algorithm built in the Origin Pro 9 software (OriginLab
Corporation, Northampton, MA). However, due to the small
data set, at this stage of the research, the PCA was conducted
only in regard to the general clinical problemdiscrimination
between cancerous and normal bladder tissues. With a bigger
data set, more in-depth analysis (for example, the classification
of the SERS spectra in regard to the tumor grade or type)
47−49
could be carried out using a more sophisticated tools for
statistical analysis. Such an analysis is planned in the future for
this research when a larger data set will be gathered.
While performing the PCA, the spectral data were analyzed
using the first five principal components. This number was
chosen because together these components explain more than
97% of the variance in the case of spectral tissue data and more
than 99% in the case of the spectral ECF data. Projections of
the data in the space of various principal component
combinations were produced and analyzed. However, the
best results of the analysis, which are shown below, were
observed using the first two principal components. The SERS
spectra of the bladder tissues and the extracellular fluid were
first analyzed in the whole spectral fingerprint region. The PCA
analysis of the collected SERS spectra analysis performed on
the whole spectral region has given unsatisfactory results in
both cases (tissues and extracellular fluid) since the SERS
spectra could not be separated into different groups (normal,
cancerous, cystitis-affected). This may be reasoned by the
intensity variation of the spectral bands of molecules which are
not associated neither with cancerous nor cystitis-affected
Table 3. SERS Marker Bands of Bladder Cancer/Cystitis Based on Analysis of Tissue and Extracellular Fluid Samples
wavenumber,
cm−1vibrational mode molecular group comments
724 A ring breathing adenine ring in DNA tissue; possible marker band of
cystitis
1222 Amide-III amide group in proteins tissue; possible marker band of
cystitis
1438 scissoring CH2; W6; 8-oxo-dG
ring stretching methylene groups in proteins and lipids; tryptophan residue in proteins; 8-
oxo-deoxyguanosine ring of DNA tissue; possible marker band of
cystitis
660 G ring breathing; C−S
stretching guanine ring in DNA; cysteine extracellular fluid; marker band
of cancer
1052 C−O, C−N, and C−C
stretching lactic acid; cysteine extracellular fluid; best marker
band of cancer
1414/1448 scissoring CH2; W6; 8-oxo-dG
ring stretching methylene groups in proteins and lipids; tryptophan residue in proteins;
8-oxo-deoxyguanosine ring of DNA extracellular fluid; marker band
of cancer
Figure 7. Difference spectra of extracellular fluid between the
cancerous and cystitis affected, and cancerous and normal bladder
tissues. The SERS spectra of different biomolecule solutions (1 mM)
are also presented for comparison. The excitation wavelength is 1064
nm.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10545
tissues. The concentration of these molecules may differ due to
other factors. Therefore, the change in the intensity of the
spectral bands related to these molecules only introduce the
unwanted variation (noise) which in result makes the PCA
analysis more difficult.
Then the analysis was performed in the regions of the
vibrational bands that can be used for identification the bladder
tissue cancer and cystitis. The following regions of the SERS
spectra, 700−750, 1190−1260, and 1400−1460 cm−1, were
selected for the bladder tissue spectra analysis. PCA performed
in the spectral regions of potential markers bands revealed that
the projections of the data of normal, cancerous, and cystitis-
affected tissue spectra partly overlap in the plane of the
principal components. No clear boundaries can be drawn
between the groups of points corresponding to the spectra of
different tissues. It can be assumed that variations in the
intensity of the vibrational bands, which have been identified as
spectral markers of cystitis-affected in bladder tissue studies,
may be random and only depend on different patient
physiology and other factors. For this reason, intensity changes
of the respective bands cannot be attributed to groups of
healthy, cancerous, or cystitis tissues that just exhibit features
that are specific to these groups.
The spectral regions 600−750, 1020−1080, and 1390−1440
cm−1were selected for the analysis of the SERS bands of the
extracellular fluid with aim of tissue discrimination. The
spectral bands observed at 660 (guanine, cysteine), 1052
(lactic acid, cysteine) and 1414 cm−1(proteins, lipids, DNA,
lactic acid) were identified as the possible spectral markers of
cancer in the study of extracellular fluid layers of tissues. In the
PCA diagrams of the spectral regions where 660 and 1414
cm−1vibrational bands are observed, the points corresponding
to the data of healthy and cancerous bladder tissues are widely
distributed (Figures S4 and S5). Most of these points overlap.
The dispersion of the points corresponding to the cystitis-
affected tissues in the PCA diagram is also high, making it
difficult to determine the possible area of their accumulation.
The PCA plot of the spectral region 1020−1080 cm−1
associated with the band that was assigned to lactic acid and
cysteine vibrations shows a clearly distinguishable group of
points corresponding to healthy patient data from all patients
(Figure 8). Based on the small dispersion of these points, it can
be assumed that the concentrations of the lactic acid and
cysteine molecules, whose vibrations are assigned to the
spectral band observed at 1052 cm−1are similar in all of the
healthy bladder tissues of different patients. In the case of the
cancer tissue data, 4 of 21 points fall into the group of points
corresponding to healthy tissue data. This may be influenced
by the amount of healthy tissue removed with the tumor
during the surgery, inaccuracies in the preparation of
extracellular fluid layers, and other factors. The remaining 17
points are sufficiently distant from the group of points
corresponding to healthy tissues to be considered as a separate
group. These points are widely distributed in comparison to
distribution of other points. A larger variance of the points can
mean a greater difference between the elements that make up
the data set. Such distribution can be explained by the fact that
different malignant cancer cells may contain different amounts
of lactic acid, or cysteine molecules, which are related to the
proliferation of cancer and metastasis. In addition, larger
tumors may have higher accumulations of these molecules.
Thus, it can be argued that healthy and cancerous tissues
contain different amount of such molecules, which is reflected
in the spectra. The points corresponding to the cystitis affected
tissues in the PCA diagram overlap with the group of healthy
tissue points. It can be stated that the changes in the intensity
of the SERS spectra of the extracellular fluid layers of cystitis-
affected tissues in the 1020−1080 cm−1region are similar to
the deviations observed in the extracellular fluid spectra of
healthy tissues. Thus, an analysis of the principal components
of the 1020−1080 cm−1spectral data revealed that 81% of
cancer tissue samples could be assigned to a separate group
with greater data variance than healthy and cystitis-affected
tissues. In comparison to the clinical standard this detection
leads to the sensitivity of around 85% and specificity of around
97% remembering that distinguishing the cystitis affected
tissue from the cancerous tissue is the sought-after result as
well.
4. CONCLUSIONS
Three types of bladder tissuesnormal, cancerous, and cystitis
affected were examined. Significant spectral differences were
observed in the SERS spectra of extracellular fluid of bladder
tissues. The intensity of the spectral band, located at 1052
cm−1and associated with lactic acid and/or cysteine, is the
highest in the SERS spectra of the extracellular fluid of
cancerous tissue, while it is less intense in the spectra of
cystitis-affected tissue and the least intense in the spectra of
normal tissue. This band can be considered as the best SERS
spectral marker of the cancerous tissue. The PCA analysis in
relation to the spectral marker has shown that the cancer tissue
can indeed be distinguished from the normal and cystitis-
affected tissues. With the limited data set used, the sensitivity
and specificity of the methods were 85% and 97%, respectively.
When the fluid is taken by the stamping technique,
morphological information of the tissue persists in the dried
fluid. However, the discrimination of the cystitis affected
tissues from the normal and cancerous is more difficult since
the intensity of the spectral bands related to internal vibrations
of Amide III (1222 cm−1), adenine (724 cm−1), and proteins/
lipids (1438 cm−1) are more intense in the spectra of cystitis-
affected tissues. Since the SERS spectroscopy is known to be
very sensitive, the use of this method instead of the
conventional Raman or the FTIR absorption spectroscopy
Figure 8. Principal component analysis (PCA) diagram of the 1020−
1080 cm−1wavenumber region of the SERS spectra of extracellular
fluid of normal, cancerous, and cystitis-affected bladder tissue samples.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10546
could increase the accuracy of detection of the cancerous tissue
areas. The sensitivity and specificity of the method can be
increased by using a larger data set, or implementing other
colloidal solutions of nanoparticles could improve the
spectroscopic analysis. Development of magneto-plasmonic
nanoparticles
80−82
with increased efficiency for the SERS
studies of the extracellular fluids is under way in our laboratory.
To prove the NIR-SERS ability as a diagnostic tool for
discrimination between cancerous and normal bladder cells via
analysis of extracellular fluid, more studies with clinical cancer
samples are required.
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acsomega.2c00058.
UV−vis absorption spectra of Ag colloidal solution;
TEM image of silver nanoparticles; size distribution of
Ag nanoparticles; Raman and SERS spectra of uric acid;
and principal component analysis diagrams of the 600−
750 and 1390−1440 cm−1wavenumber regions of the
SERS spectra of the extracellular fluid of normal,
cancerous, and cystitis affected bladder tissue samples
(PDF)
■AUTHOR INFORMATION
Corresponding Authors
Martynas Velička −Institute of Chemical Physics, Faculty of
Physics, Vilnius University, LT-10257 Vilnius, Lithuania;
Email: martynas.velicka@ff.vu.lt
Gediminas Niaura −Institute of Chemical Physics, Faculty of
Physics, Vilnius University, LT-10257 Vilnius, Lithuania;
Department of Organic Chemistry, Center for Physical
Sciences and Technology (FTMC), LT 10257 Vilnius,
Lithuania; orcid.org/0000-0002-2136-479X;
Email: gediminas.niaura@ftmc.lt
Authors
Edvinas Zacharovas −Institute of Chemical Physics, Faculty
of Physics, Vilnius University, LT-10257 Vilnius, Lithuania
Gediminas Platkevičius −Clinic of Gastroenterology,
Nephrourology, and Surgery, Institute of Clinical Medicine,
Faculty of Medicine, Vilnius University, LT-03101 Vilnius,
Lithuania
Albertas C
̌
ekauskas −Clinic of Gastroenterology,
Nephrourology, and Surgery, Institute of Clinical Medicine,
Faculty of Medicine, Vilnius University, LT-03101 Vilnius,
Lithuania
Aru̅nas Z
̌
elvys −Clinic of Gastroenterology, Nephrourology,
and Surgery, Institute of Clinical Medicine, Faculty of
Medicine, Vilnius University, LT-03101 Vilnius, Lithuania
Valdas S
̌
ablinskas −Institute of Chemical Physics, Faculty of
Physics, Vilnius University, LT-10257 Vilnius, Lithuania
Complete contact information is available at:
https://pubs.acs.org/10.1021/acsomega.2c00058
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This project has received funding from the European Regional
Development Fund (Project No. 01.2.2-LMT-K-718-03-0078)
under a grant agreement with the Research Council of
Lithuania (LMTLT). The authors gratefully acknowledge the
Center of Spectroscopic Characterization of Materials and
Electronic/Molecular Processes (SPECTROVERSUM Infra-
structure) for use of Raman spectrometer.
■REFERENCES
(1) Ferlay, J.; Soerjomataram, I.; Ervik, M.; Dikshit, R.; Eser, S.;
Mathers, C.; Rebelo, M.; Parkin, D. M.; Forman, D.; Bray, F.
Estimated cancer incidence, mortality and prevalence worldwide in
2012. 2013. 2015. GLOBOCAN, 2012, v1.0, accessed December
2020; http://globocan.iarc.fr/Default.asp.
(2) Sylvester, R. J.; Van Der Meijden, A. P. M.; Oosterlinck, W.;
Witjes, J. A.; Bouffioux, C.; Denis, L.; Newling, D. W. W.; Kurth, K.
Predicting recurrence and progression in individual patients with stage
Ta T1 bladder cancer using EORTC risk tables: a combined analysis
of 2596 patients from seven EORTC trials. Eur. Urol. 2006,49, 466−
477.
(3) Curtis, S. Milken Institute 2016 Report on Bladder Cancer; Milken
Inst.: 2016.
(4) Mowatt, G.; N’Dow, J.; Vale, L.; Nabi, G.; Boachie, C.; Cook, J.
A.; Fraser, C.; Griffiths, T. R. L. Photodynamic diagnosis of bladder
cancer compared with white light cystoscopy: Systematic review and
meta-analysis. Int. J. Technol. Assess Health Care 2011,27,3−10.
(5) Walsh, P. C.; Retik, A. B.; Vaughan, Jr., E. D.; Wein, A. J.;
Kavoussi, L. R.; Novick, A. C.; Partin, A. W.; Peters, C. A. Campbell’s
Urology, 8th ed.; Elsevier: 2002.
(6) Moch, H.; Cubilla, A. L.; Humphrey, P. A.; Reuter, V. E.;
Ulbright, T. M. The 2016 WHO Classification of Tumours of the
Urinary System and Male Genital Organs-Part A: Renal, Penile, and
Testicular Tumours. Eur. Urol. 2016,70,93−105.
(7) Birkhäuser, F. D.; Studer, U. E.; Froehlich, J. M.; Triantafyllou,
M.; Bains, L. J.; Petralia, G.; Vermathen, P.; Fleischmann, M.;
Thoeny, H. C. Combined ultrasmall superparamagnetic particles of
iron oxide-enhanced and diffusion-weighted magnetic resonance
imaging facilitates detection of metastases in normal-sized pelvic
lymph nodes of patients with bladder and prostate cancer. Eur. Urol.
2013,64, 953−960.
(8) Panebianco, V.; Narumi, Y.; Altun, E.; Bochner, B. H.;
Efstathiou, J. A.; Hafeez, S.; Huddart, R.; Kennish, S.; Lerner, S.;
Montironi, R.; Muglia, V. F.; Salomon, G.; Thomas, S.; Vargas, H. A.;
Witjes, J. A.; Takeuchi, M.; Barentsz, J.; Catto, J. W. F. Multi-
parametric Magnetic Resonance Imaging for Bladder Cancer:
Development of VI-RADS (Vesical Imaging-Reporting And Data
System). Eur. Urol. 2018,74, 294−306.
(9) Su, K. A.; Lee, W. L. Fourier transform infrared spectroscopy as a
cancer screening and diagnostic tool: A review and prospects. Cancers
2020,12, 115.
(10) Kanmalar, M.; Abdul Sani, S. F.; Kamri, N. I. N. B.; Said, N. A.
B. M.; Jamil, A. H. B. A.; Kuppusamy, S.; Mun, K. S.; Bradley, D. A.
Raman spectroscopy biochemical characterization of bladder cancer
cisplatin resistance regulated by FDFT1: a review. Cell. Mol. Biol. Lett.
2022,27,9.
(11) Chen, H.; Li, X.; Broderick, N.; Liu, Y.; Zhou, Y.; Han, J.; Xu,
W. Identification and characterization of bladder cancer by low-
resolution fiber-optic Raman spectroscopy. J. Biophotonics 2018,11,
e201800016.
(12) Kong, K.; Kendall, C.; Stone, N.; Notingher. Raman
spectroscopy for medical diagnostics –From in-vitro biofluid assays
to in-vivo cancer detection. Adv. Drug. Delivery Rev. 2015,89, 121−
134.
(13) Bonifacio, A.; Cervo, S.; Sergo, V. Label-free surface-enhanced
Raman spectroscopy of biofluids: fundamental aspects and diagnostic
applications. Anal. Bioanal. Chem. 2015,407, 8265−8277.
(14) Pilot, R.; Signorini, R.; Durante, C.; Orian, L.; Bhamidipati, M.;
Fabris, L. A review on surface-enhanced Raman scattering. Biosensors
2019,9, 57.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10547
(15) Pyrak, E.; Krajczewski, J.; Kowalik, A.; Kudelski, A.; Jaworska,
A. Surface enhanced Raman spectroscopy for DNA biosensors −How
far are we? Molecules 2019,24, 4423.
(16) Jaworska, A.; Malek, K.; Kudelski, A. Intracellular pH −
Advantages and pitfalls of surface-enhanced Raman scattering and
fluorescence microscopy −A review. Spectrochim. Acta, Part A 2021,
251, 119410.
(17) Aydin, O.; Altas, M.; Kahraman, M.; Bayrak, O. F.; Culha, M.
Differentiation of healthy brain tissue and tumors using surface-
enhanced Raman scattering. Appl. Spectrosc. 2009,63, 1095−1100.
(18) Karabeber, H.; Huang, R.; Iacono, P.; Samii, J. M.; Pitter, K.;
Holland, E. C.; Kircher, M. F. Guiding Brain Tumor Resection Using
Surface-Enhanced Raman Scattering Nanoparticles and a Hand-Held
Raman Scanner. ACS Nano 2014,8, 9755−9766.
(19) Butler, H. J.; Brennan, P. M.; Cameron, J. M.; Finlayson, D.;
Hegarty, M. G.; Jenkinson, M. D.; Palmer, D. S.; Smith, B. R.; Baker,
M. J. Development of high-throughput ATR-FTIR technology for
rapid triage of brain cancer. Nat. Commun. 2019,10, 4501.
(20) Kowalska, A. A.; Berus, S.; Szleszkowski, L.; Kamińska, A.;
Kmiecik, A.; Ratajczak-Wielgomas, K.; Jurek, T.; Zadka, Ł. Brain
tumor homogenates analysed by surface-enhanced Raman spectros-
copy: Discrimination among healthy and cancer cells. Spectrochim.
Acta, Part A 2020,231, 117769.
(21) Ali, M. H. M.; Rakib, F.; Al-Saad, K.; Al-Saady, R.;
Goormaghtigh,E.AnInnovative Platform Merging Elemental
Analysis and Ftir Imaging for Breast Tissue Analysis. Sci. Rep. 2019,
9, 9854.
(22) Shen, L.; Du, Y.; Wei, N.; Li, Q.; Li, S.; Sun, T.; Xu, S.; Wang,
H.; Man, X.; Han, B. SERS studies on normal epithelial and cancer
cells derived from clinical breast cancer specimens. Spectrochim. Acta,
Part A 2020,237, 118364.
(23) Moisoiu, V.; Socaciu, A.; Stefancu, A.; Iancu, S. D.; Boros, I.;
Alecsa, C. D.; Rachieriu, C.; Chiorean, A. R.; Eniu, D.; Leopold, N.;
Socaciu, C.; Eniu, D. T. Breast cancer diagnosis by surface-enhanced
Raman scattering of urine. Appl. Sci. 2019,9, 806.
(24) Pramanik, A.; Mayer, J.; Patibandla, S.; Gates, K.; Gao, Y.;
Davis, D.; Seshadri, R.; Ray, P. C. Mixed-dimensional heterostructure
material-based SERS for trace level identification of breast cancer-
derived exosomes. ACS Omega 2020,5, 16602−16611.
(25) Su, K.-Y.; Lee, W.-L. Fourier Transform Infrared Spectroscopy
as a Cancer Screening and Diagnostic Tool: A Review and Prospect.
Cancers 2020,12, 115.
(26) Guerrini, L.; Alvarez-Puebla, R. A. Surface-Enhanced Raman
Spectroscopy in Cancer Diagnosis, Prognosis and Monitoring. Cancers
2019,11, 748.
(27) Niciński, K.; Krajczewski, J.; Kudelski, A.; Witkowska, E.;
Trzcińska-Danielewicz, J.; Girstun, A.; Kamińska, A. Detection of
circulating tumor cells in blood by shell-isolated nanoparticle-
enhanced Raman spectroscopy (SHINERS) in microfluidic device.
Sci. Rep. 2019,9, 9267.
(28) Bizzarri, A. R.; Cannistraro, S. Toward cancer diagnostics of the
tumor suppressor p53 by surface enhanced Raman spectroscopy.
Sensors 2020,20, 7153.
(29) Pucetaite, M.; Velicka, M.; Urboniene, V.; Ceponkus, J.;
Bandzeviciute, R.; Jankevicius, F.; Zelvys, A.; Sablinskas, V.; Steiner,
G. Rapid intra-operative diagnosis of kidney cancer by attenuated
total reflection infrared spectroscopy of tissue smears. J. Biophotonics
2018,11, e201700260.
(30) Velicka, M.; Pucetaite, M.; Urboniene, V.; Ceponkus, J.;
Jankevicius, F.; Sablinskas, V. Detection of cancerous kidney tissue by
means of SERS spectroscopy of extracellular fluid. J. Raman Spectrosc.
2017,48, 1744−1754.
(31) Zhang, Y.; Mi, X.; Tan, X.; Xiang, R. Recent Progress on Liquid
Biopsy Analysis using Surface-Enhanced Raman Spectroscopy.
Theranostics 2019,9, 491−525.
(32) Sitnikova, V. E.; Kotkova, M. A.; Nosenko, T. N.; Kotkova, T.
N.; Martynova, D. M.; Uspenskaya, M. V. Breast cancer detection by
ATR-FTIR spectroscopy of blood serum and multivariate data-
analysis. Talanta 2020,214, 120857.
(33) Moisoiu, V.; Iancu, S. D.; Stefancu, A.; Moisoiu, T.; Pardini, B.;
Dragomir, M. P.; Crisan, N.; Avram, L.; Crisan, D.; Andras, I.; Fodor,
D.; Leopold, L. F.; Socaciu, C.; Bálint, Z.; Tomuleasa, C.; Elec, F.;
Leopold, N. SERS liquid biopsy: An emerging tool for medical
diagnosis. Colloids Surf., B 2021,208, 112064.
(34) Al-Muslet, N. A.; Ali, E. E. Spectroscopic analysis of bladder
cancer tissues using Fourier transform infrared spectroscopy. J. Appl.
Spectrosc. 2012,79, 139−142.
(35) Witzke, K. E.; Großerueschkamp, F.; Jütte, H.; Horn, M.;
Roghmann, F.; Von Landenberg, N.; Bracht, T.; Kallenbach-Thieltges,
A.; Käfferlein, H.; Brüning, T.; Schork, K.; Eisenacher, M.; Marcus,
K.; Noldus, J.; Tannapfel, A.; Sitek, B.; Gerwert, K. Integrated Fourier
Transform Infrared Imaging and Proteomics for Identification of a
Candidate Histochemical Biomarker in Bladder Cancer. Am. J. Pathol.
2019,189, 619−631.
(36) Gok, S.; Aydin, A. Z.; Sural, Y. S.; Zorlu, F.; Bayol, U.;
Severcan, F. Bladder cancer diagnosis from bladder wash by Fourier
transform infrared spectroscopy as a novel test for tumor recurrence.
J. Biophotonics 2016,9, 967−975.
(37) Jin, D.; Chen, H.; Cao, M.; Yang, G.; Xue, W.; Huang, Y. SERS
measurement of the bladder cancer cells with the nanoparticles. Pak. J.
Pharm. Sci. 2015,28, 1853−1856.
(38) Davis, R. M.; Kiss, B.; Trivedi, D. R.; Metzner, T. J.; Liao, J. C.;
Gambhir, S. S. Surface-Enhanced Raman Scattering Nanoparticles for
Multiplexed Imaging of Bladder Cancer Tissue Permeability and
Molecular Phenotype. ACS Nano 2018,12, 9669−9679.
(39) Chen, S.; Zhu, S.; Cui, X.; Xu, W.; Kong, C.; Zhang, Z.; Qian,
W. Identifying non-muscle-invasive and muscle-invasive bladder
cancer based on blood serum surface-enhanced Raman spectroscopy.
Biomed. Opt. Express 2019,10, 3533−3544.
(40) Chuang, Y.-T.; Cheng, T.-Y.; Kao, T.-L.; Liao, M.-Y. Hollow
AuxCu1−x alloy nanoshells for surface-enhanced Raman-based
tracking of bladder cancer cells followed by triggerable secretion
removal. ACS Appl. Nano Mater. 2020,3, 7888−7898.
(41) Liang, X.; Zhang, P.; Ma, M.; Yang, T.; Zhao, X.; Zhang, R.;
Jing, M.; Song, R.; Wang, L.; Fan, J. Multiplex ratiometric gold
nanoprobes based on surface-enhanced Raman scattering enable
accurate molecular detection and imaging of bladder cancer. Nano
Research 2021,DOI: 10.1007/s12274-021-3902-1.
(42) Liu, Y.; Huang, L. Q.; Wang, J.; Tong, H. M.; Yang, L.; Zhao, L.
H.; Zhang, W. W.; Wang, L.; Zhu, J. Fabrication of silver ordered
nanoarrays SERS-active substrates and their applications in bladder
cancer cells detection. Spectrosc. Spectral Anal. 2012,32, 386−390.
(43) Pallaoro, A.; Mirsafavi, R. Y.; Culp, W. T. N.; Braun, G. B.;
Meinhart, C. D.; Moskovits, M. Screening for canine transitional cell
carcinoma (TCC) by SERS-based quantitative urine cytology.
Nanomedicine: NBM 2018,14, 1279−1287.
(44) Jin, D.; Wang, X. T.; Fu, B.; Li, T. H.; Chen, N.; Chen, Z. Y.;
Chen, H. G.; Liu, S. P. Raman spectroscopy of luminal subtype and
basal subtype muscle invasive bladder cancer. Int. J. Clin. Exp. Med.
2019,12, 5447−5453.
(45) Zhang, W.; Jiang, L.; Diefenbach, R. J.; Campbell, D. H.; Walsh,
B. J.; Packer, N. H.; Wang, Y. Enabling sensitive phenotypic profiling
of cancer-derived small extracellular vesicles using surface-enhanced
Raman spectroscopy nanotags. ACS Sens. 2020,5, 764−771.
(46) Feng, E.; Zheng, T.; Tian, Y. Dual-mode Au nanoprobe based
on surface enhancement Raman scattering and colorimetry for
sensitive determination of telomerase activity both in cell extracts
and in the urine of patients. ACS Sens. 2019,4, 211−217.
(47) Hu, D.; Xu, X.; Zhao, Z.; Li, C.; Tian, Y.; Liu, Q.; Shao, B.;
Chen, S.; Zhao, Y.; Li, L.; Bi, H.; Chen, A.; Fu, C.; Cui, X.; Zeng, Y.
Detecting urine metabolites of bladder cancer by surface-enhanced
Raman spectroscopy. Spectrochim. Acta, Part A 2021,247, 119108.
(48) Yang, Y.-T.; Hsu, I.-L.; Cheng, T.-Y.; Wu, W.-J.; Lee, C.-W.; Li,
T.-J.; Cheung, C. I.; Chin, Y.-C.; Chen, H.-C.; Chiu, Y.-C.; Huang, C.-
C.; Liao, M.-Y. Off-resonance SERS nanoprobe-targeted screen of
biomarkers for antigens recognition of bladder normal and aggressive
cancer cells. Anal. Chem. 2019,91, 8213−8220.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10548
(49) Zhang, Y.; Lai, X.; Zeng, Q.; Li, L.; Lin, L.; Li, S.; Liu, Z.; Su,
C.; Qi, M.; Guo, Z. Classifying low-grade and high-grade bladder
cancer using label-free serum surface-enhanced Raman spectroscopy
and support vector machine. Laser Phys. 2018,28, 035603.
(50) Li, S.; Li, L.; Zeng, Q.; Zhang, Y.; Guo, Z.; Liu, Z.; Jin, M.; Su,
C.; Lin, L.; Xu, J.; Liu, S. Characterization and noninvasive diagnosis
of bladder cancer with serum surface enhanced Raman spectroscopy
and genetic algorithms. Sci. Rep. 2015,5, 9582.
(51) Chen, S.; Zhu, S.; Cui, X.; Xu, W.; Kong, C.; Zhang, Z.; Qian,
W. Identifying non-muscle-invasive and muscle-invasive bladder
cancer based on blood serum surface-enhanced Raman spectroscopy.
Biomed. Opt. Express 2019,10, 3533−3544.
(52) Davis, R. M.; Kiss, B.; Trivedi, D. R.; Metzner, T. J.; Liao, J. C.;
Gambhir, S. S. Surface-enhanced Raman scattering nanoparticles for
multiplexed imaging of bladder cancer tissue permeability and
molecular phenotype. ACS Nano 2018,12, 9669−9679.
(53) Bonifacio, A.; Dalla Marta, S.; Spizzo, R.; Cervo, S.; Steffan, A.;
Colombatti, A.; Sergo, V. Surface-enhanced Raman spectroscopy of
blood plasma and serum using Ag and Au nanoparticles: a systematic
study. Anal. Bioanal. Chem. 2014,406, 2355−2365.
(54) Cao, J.; Zhao, D.; Mao, Q. A highly reproducible and sensitive
fiber SERS probe fabricated by direct synthesis of closely packed
AgNPs on the silanized fiber taper. Analyst 2017,142, 596−602.
(55) Lee, P. C.; Meisel, D. Adsorption and surface-enhanced Raman
of dyes on silver and gold sols. J. Phys. Chem. 1982,86, 3391−3395.
(56) MacCuspie, R. I. Colloidal stability of silver nanoparticles in
biologically relevant conditions. J. Nanopart. Res. 2011,13, 2893−
2908.
(57) Valenti, L. E.; Giacomelli, C. E. Stability of silver nanoparticles:
agglomeration and oxidation in biological relevant conditions. J.
Nanopart. Res. 2017,19, 156.
(58) Langer, J.; Jimenez De Aberasturi, D.; Aizpurua, J.; Alvarez-
Puebla, R. A.; Auguié, B.; Baumberg, J. J.; Bazan, G. C.; Bell, S. E. J.;
Boisen, A.; Brolo, A. G.; et al. Present and future of surface-enhanced
Raman scattering. ACS Nano 2020,14,28−117.
(59) Munro, C. H.; Smith, W. E.; Garner, M.; Clarkson, J.; White, P.
C. Characterization of the surface of a citrate-reduced colloid
optimized for use as a substrate for surface-enhanced resonance
Raman scattering. Langmuir 1995,11, 3712−3720.
(60) Perales-Rondon, J. V.; Hernandez, S.; Martin-Yerga, S.; Fanjul-
Bolado, P.; Heras, A.; Colina, A. Electrochemical surface oxidation
enhanced Raman scattering. Electrochim. Acta 2018,282, 377−383.
(61) Kocherbitov, V.; Latynis, J.; Misiu̅nas, A.; Barauskas, J.; Niaura,
G. Hydration of lysozyme studied by Raman spectroscopy. J. Phys.
Chem. B 2013,117, 4981−4992.
(62) Matulaitienė, I.; Kuodis, Z.; Matijoška, A.; Eicher-Lorka, O.;
Niaura, G. SERS of the positive charge bearing pyridinium ring
terminated self-assembled monolayers: Structure and bonding spectral
markers. J. Phys. Chem. C 2015,119, 26481−26492.
(63) Mert, S.; Ozbek, E.; Otunctemur, A.; Culha, M. Kidney tumor
staging using surface-enhanced Raman scattering. J. Biomed. Optics
2015,20 (4), 047002.
(64) Mert, S.; Culha, M. Surface-Enhanced Raman Scattering-Based
Detection of Cancerous Renal Cells. Appl. Spectrosc. 2014,68, 617−
624.
(65) Zdaniauskienė, A.; Charkova, T.; Ignatjev, I.; Melvydas, V.;
Garjonytė, R.; Matulaitienė, I.; Talaikis, M.; Niaura, G. Shell-isolated
nanoparticle-enhanced Raman spectroscopy for characterization of
living yeast cells. Spectrochim. Acta, Part A 2020,240, 118560.
(66) Premasiri, W. R.; Lee, J. C.; Sauer-Budge, A.; Théberge, R.;
Costello, C. E.; Ziegler, L. D. The biochemical origins of the surface-
enhanced Raman spectra of bacteria: a metabolimics profiling by
SERS. Anal. Bioanal. Chem. 2016,408, 4631−4647.
(67) Czamara, K.; Majzner, K.; Pacia, M. Z.; Kochan, K.; Kaczor, A.;
Baranska, M. Raman spectroscopy of lipids: a review. J. Raman
Spectrosc. 2015,46,4−20.
(68) Szymanska-Chargot, M.; Chylinska, M.; Pieczywek, P. M.;
Rösch, P.; Schmitt, M.; Popp, J.; Zdunek, A. Raman imaging changes
in the polysaccharides distribution in the cell wall during apple fruit
development and senescence. Planta 2016,243, 935−945.
(69) Barrett, T. W. Laser Raman spectra of mono-, oligo- and
polysaccharides in solution. Spectrochim. Acta, Part A 1981,37, 233−
239.
(70) Stewart, S.; Fredericks, P. M. Surface-enhanced Raman
spectroscopy of peptides and proteins adsorbed on an electrochemi-
cally prepared silver surface. Spectrochim. Acta, Part A 1999,55
(1999), 1615−1640.
(71) Podstawka, E.; Niaura, G. Potential-dependent characterization
of bombesin adsorbed states on roughened Ag, Au, and Cu electrode
surfaces at physiological pH. J. Phys. Chem. B 2009,113, 10974−
10983.
(72) Kundu, L. M.; Loppnow, G. R. Direct detection of 8-oxo-
deoxyguanosine using UV resonance Raman spectroscopy. Photochem.
Photobiol. 2006,83, 600−602.
(73) Yao, G.; Huang, Q. DFT and SERS Study of L-Cysteine
Adsorption on the Surface of Gold Nanoparticles. J. Phys. Chem. C
2018,122, 15241−15251.
(74) Combs, J. A.; DeNicola, G. M. The Non-Essential Amino Acid
Cysteine Becomes Essential for Tumor Proliferation and Survival.
Cancers 2019,11, 678.
(75) Serpa, J. Cysteine as a Carbon Source, a Hot Spot in Cancer
Cells Survival. Front. Oncol. 2020,10, 947.
(76) Long, J.; Zhang, C.-J.; Zhu, N.; Du, K.; Yin, Y.-F.; Tian, X.;
Liao, D.-F.; Qin, L. Lipid metabolism and carcinogenesis, cancer
development. Am. J. Cancer Res. 2018,8, 778−791.
(77) Warburg, O. On the Origin of Cancers Cells. Science 1956,123,
309−314.
(78) Cheng, Y.; Yang, X.; Deng, X.; Zhang, X.; Li, P.; Tao, J.; Qin,
C.; Wei, J.; Lu, Q. Metabolomics in bladder cancer: a systematic
review. Int. J. Clin. Exp. Med. 2015,8, 11052−11063.
(79) Schärer, O. D. Chemistry and biology of DNA repair. Angew.
Chem., Int. Ed. Engl. 2003,42, 2946−2974.
(80) Mosier-Boss, P. A. Review of SERS substrates for chemical
sensing. Nanomaterials 2017,7, 142.
(81) Wang, C.; Meloni, M. M.; Wu, X.; Zhuo, M.; He, T.; Wang, J.;
Wang, C.; Dong, P. Magnetic plasmonic particles for SERS-based
bacteria sensing: A review. AIP Adv. 2019,9, 010701.
(82) Lai, H.; Xu, F.; Wang, L. A review of the preparation and
application of magnetic nanoparticles for surface-enhanced Raman
scattering. J. Mater. Sci. 2018,53, 8677−8698.
ACS Omega http://pubs.acs.org/journal/acsodf Article
https://doi.org/10.1021/acsomega.2c00058
ACS Omega 2022, 7, 10539−10549
10549
