Abstract

   Cervical cancer remains one of the most prevalent cancers among females
   worldwide. Therefore, it is important to discover new biomarkers for
   early diagnosis of cervical intraepithelial neoplasia (CIN) and
   cervical cancer, preferably non-invasive ones. In the present study, we
   aimed to identify unique metabolic signatures for CINs and cervical
   cancers using global and targeted metabolomic profiling. Plasma samples
   (69 normal, 55 CIN1, 42 CIN2/3, and 60 cervical cancer) were examined
   by ultra-performance liquid chromatography-quadrupole-time-of-flight
   mass spectrometry (UPLC-QTOF-MS) coupled with multivariate statistical
   analysis. Metabolic pathways were analyzed using the integrated
   web-based tool MetaboAnalyst. A multivariate logistic regression
   analysis was conducted to evaluate the combined association of
   metabolites and human papillomavirus (HPV) status with the risk of
   cervical carcinogenesis. A total of 28 metabolites exhibiting
   discriminating levels among normal, CIN, and cervical cancer patients
   (Kruskal–Wallis test p < 0.05) were identified in the global profiling
   analysis. The pathway analysis showed significantly altered alanine,
   aspartate, and glutamate metabolic pathways (FDR p-value < 0.05) in
   both the discovery and validation phases. Seven metabolites (AMP,
   aspartate, glutamate, hypoxanthine, lactate, proline, and
   pyroglutamate) were discriminated between CINs and cervical cancer
   versus normal (area under the curve (AUC) value > 0.8). The levels of
   these metabolites were significantly high in patients versus normal (p
   < 0.0001) and were associated with increased risk of developing CIN2/3
   and cervical cancer. Additionally, elevated levels of the seven
   metabolites combined with positive HPV status were correlated with
   substantial risk of cancer progression. These results demonstrated that
   metabolomics profiling is capable of distinguishing CINs and cervical
   cancers from normal and highlighted potential biomarkers for the early
   detection of cervical carcinogenesis.

   Keywords: biomarker, cervical cancer, human papillomavirus, metabolomic
   analysis, metabolic pathways, plasma profiling

1. Introduction

   Cervical cancer is a common malignant disease in women around the
   world. It is the third most commonly diagnosed cancer (~485,000 cases)
   and the fourth leading cause of cancer-related deaths (236,000) per
   year worldwide [[39]1,[40]2]. The main cause of cervical cancer is
   persistent oncogenic human papillomavirus (HPV) infection. Cervical
   intraepithelial neoplasia (CIN) 1–3 and carcinoma in situ are the
   precursor lesions of cervical cancer [[41]3]. Other factors such as
   sexually transmitted infections, oral contraceptive use, smoking
   status, parity, and diet also contribute to the development of cervical
   cancer [[42]4]. Patients diagnosed with cervical cancer often show no
   obvious signs or symptoms at early stages, which fact can lead to
   misdiagnosis. The incidence of cervical cancer worldwide varies
   significantly due to advancements in research and precautionary
   measures taken by developed countries [[43]5]. Incidence decline can be
   attributed to regular application of Pap tests, which can reveal CINs
   and carcinoma in situ before they develop into cervical cancer, and
   vaccination [[44]6]. However, current diagnostics for cervical cancer
   are based on histological and morphological examinations, which lack
   sensitivity and specificity [[45]7].

   Recent developments in molecular biology, proteomics, and metabolomics
   have inaugurated a new era marked by the discovery of biomarkers for
   cervical cancer [[46]7,[47]8]. Metabolomic research for cervical cancer
   treatment applications, in particular, has attracted immense interest
   globally. Metabolites, the end products of several biological
   processes, show potential as accurate biomarkers identifying upstream
   biological processes such as genetic mutations and environmental
   changes [[48]9,[49]10]. Metabolomics based on platforms such as nuclear
   magnetic resonance (NMR) spectroscopy or mass spectrometry (MS)
   combined with chromatography has been utilized for comprehensive
   analysis and quantification of metabolites in biological systems
   [[50]11,[51]12,[52]13]. Metabolomics as applied to cancer research has
   been a powerful tool for identification of metabolic changes in cancer
   development and progression and discovery of non-invasive biomarkers
   for cancer prediction, monitoring, and diagnosis
   [[53]14,[54]15,[55]16]. Changes in metabolic profiles and pathways can
   provide a better understanding of dysregulated metabolism in tumor
   initiation and progression compared with normal (healthy) controls
   [[56]17]. A well-known biomarker for cervical cancer is the squamous
   cell carcinoma antigen, which is elevated in 50%, 71%, and 82% of
   patients with stages I, II, and III–IV disease, respectively [[57]18].
   Circulating antibodies [[58]19], CDKN3 mRNA [[59]20], amino acids
   [[60]21], and lipids [[61]22] also have been studied as potential
   biomarkers for cervical cancer. However, their diagnostic accuracy and
   predictive performance remain uncertain.

   To the best of our knowledge, this is the first study of its kind to
   identify, through metabolomic analysis, dysregulated metabolic pathways
   and potential biomarkers for CINs and cervical cancers. We performed
   ultra-performance liquid chromatography (UPLC)-MS-based metabolomic
   profiling in both CINs/cancer patients and normal controls to identify
   circulating biomarkers of disease progression and validate identified
   metabolite biomarkers. This global and targeted approach consisting of
   both discovery and validation steps could be helpful for discovering
   new biomarkers for risk prediction and early-disease diagnosis.

2. Results

2.1. General Characteristics of Study Participants

   The baseline characteristics of the patients and normal (healthy)
   controls included in the study are listed in [62]Table 1. The median
   ages calculated for the normal, CIN1, CIN2/3, and cervical cancer
   groups were 48, 35, 39.5, and 50 years, respectively. Among these
   groups, 43.5%, 54.6%, 71.4%, and 78.3% were positive for HPV infection,
   respectively. Significant differences (p < 0.05) in age, body mass
   index (BMI), HPV infection, marital status, education, postmenopausal
   status, pregnancy, and smoking status were observed among the groups;
   the exception was oral contraceptive use (p = 0.0949). Notably, the
   highest percentages for BMI, HPV, postmenopausal status, and positive
   pregnancy were recorded in the cervical cancer group.

Table 1.

   General characteristics of study subjects.
   Variables Normal, n = 69 CIN1, n = 55 CIN2/3, n = 42 CX CAN, n = 60 p
   ^a
   Age (years) 48 (43, 51) 35 (31, 40) 39.5 (33, 49) 50 (42, 51) <0.0001
   BMI (Kg/m^2) 21.64 (20.5, 23.2) 20.6 (19.4, 21.9) 20.8 (19.8, 23.4)
   23.2 (20.6, 25.7) 0.0003
   HPV Positive 30 (43.5) 30 (54.6) 30 (71.4) 47 (78.3) 0.0002
   Marital status Single 4 (5.8) 21 (38.2) 7 (20) 4 (6.67) <0.0001
   Married 59 (85.5) 29 (52.7) 24 (68.6) 47 (78.3)
   Divorce, bereavement 6 (8.7) 5 (9.1) 4 (11.4) 9 (15)
   Education ≤9 y 12 (17.4) 2 (3.6) 6 (17.1) 30 (50) <0.0001
   9–12 y 24 (34.8) 19 (34.6) 13 (37.1) 21 (35)
   ≥12 y 33 (47.8) 34 (61.8) 16 (45.7) 9 (15.0)
   Postmenopausal Yes 28 (40.6) 4 (7.3) 8 (22.9) 34 (56.7) <0.0001
   Pregnancy No 4 (5.8) 21 (38.2) 9 (25.7) 5 (8.3) <0.0001
   Yes 65 (94.2) 34 (61.8) 26 (74.3) 55 (91.7)
   Oral contraceptive Yes 11 (15.9) 8 (14.8) 12 (34.3) 9 (17.7) 0.0949
   Smoking status Yes, include past 8 (11.8) 18 (32.7) 7 (20) 7 (11.7)
   0.0097
   [63]Open in a new tab

   Data are present as median (25%, 75%), number (%). CIN1: cervical
   intraepithelial neoplasia 1, CIN2/3: cervical intraepithelial neoplasia
   2 or 3, CX CAN: cervical cancer. ^a Kruskal–Wallis test in continuous
   variables and chi-square test in categorical data.

2.2. Global Metabolic Profiling of Plasma by UPLC-QTOF-MS

   We performed global profiling of the polar metabolites in the plasma
   samples to identify altered metabolites in the CIN and cervical cancer
   patients. During the profiling, the analytical robustness of
   ultra-performance liquid chromatography-quadrupole-time-of-flight mass
   spectrometry (UPLC-QTOF-MS) quality control (QC) samples was repeatedly
   analyzed. In those samples, the reproducibility of the polar metabolite
   features was evaluated by coefficients of variation (CV); metabolite
   features showing large variations were removed (CV > 20%) before
   conducting the statistical analysis. In the UPLC-QTOF-MS dataset, a
   total of 1920 metabolite features were detected in the positive mode
   and 2563 were detected in the negative mode. After annotation, we
   obtained 52 metabolite features in the positive mode and 40 in the
   negative mode. To determine whether the metabolic profiles (CV < 20%)
   of the CINs and cervical cancers were different from the normal ones, a
   multivariate statistical analysis using the principal component
   analysis (PCA) model was conducted. The score plots obtained from the
   PCA model are presented in [64]Figure 1A,B. The R^2X values of the PCA
   analysis were >0.6 for both the positive mode (R^2X = 0.635) and the
   negative mode (R^2X = 0.666). We excluded 26 PCA-based-outlier plasma
   samples (1 CIN1, 17 CIN2/3, 8 cervical cancer) from further analysis.
   The PCA analysis demonstrated that the four groups were not clearly
   discriminated; instead, the results had clustered into two groups: A
   normal/CIN1 group and a CIN2 or 3/cervical cancer group. Importantly,
   the QC samples were clustered together on the PCA score plot, which
   indicated the robustness of our polar profiling platform ([65]Figure
   S1).

Figure 1.

   [66]Figure 1
   [67]Open in a new tab

   Score plots of principal component analysis (PCA) from metabolite
   spectra of (A) positive (R^2X = 0.635, and Q^2 = 0.472) and (B)
   negative (R^2X = 0.666, and Q^2 = 0.399) modes of ultra-performance
   liquid chromatography-quadrupole-time-of-flight mass spectrometry
   (UPLC-QTOF-MS). Plots of the PCA analysis reveal no clear separations
   of the four groups, but clustered into two groups: normal/ cervical
   intraepithelial neoplasia (CIN)1 group and CIN2 or 3/cervical cancer
   group. CIN1: Cervical intraepithelial neoplasia 1, CIN2/3: Cervical
   intraepithelial neoplasia 2 or 3, CX CAN: Cervical cancer.

2.3. Differential Mapping of Metabolites in Pathway Analysis

   To explore the metabolic pathways that potentially contribute to
   cervical cancer progression, we carried out a global metabolic pathway
   analysis (MetPA website: [68]www.metaboanalyst.ca). By logarithmic
   transformation, totals of 37, 29, and 33 pathways from significant
   metabolites were obtained for normal and cervical cancer, CIN1 and
   cervical cancer, and normal + CIN1 and CIN2/3 + cervical cancer,
   respectively (false discovery rate (FDR) p-value < 0.05), as shown in
   [69]Figure 2. Small p-values and large pathway-impact values indicated
   highly influential pathways. Based on the impact values and p-values,
   the pathways of alanine, aspartate and glutamate metabolism, arginine
   and proline metabolism, taurine and hypotaurine metabolism, and
   pyruvate metabolism were significantly altered in the cases of normal
   versus cervical cancer, CIN1 versus cervical cancer, and normal + CIN1
   versus CIN2/3 + cervical cancer ([70]Figure S2; FDR p-value < 0.05;
   impact value > 0.3).

Figure 2.

   [71]Figure 2
   [72]Open in a new tab

   Metabolic pathway analysis in cervical carcinogenesis. Global pathway
   analysis was performed using MetaboAnalyst: (a) Normal and cervical
   cancer, (b) CIN1 and cervical cancer, (c) normal, CIN1, and CIN2/3,
   cervical cancer. ① Alanine, aspartate, and glutamate metabolism,
   ②Arginine and proline metabolism, ③ Taurine and hypotaurine metabolism,
   ④ Pyruvate metabolism. N: Normal, CIN1: Cervical intraepithelial
   neoplasia 1, CIN2/3: Cervical intraepithelial neoplasia 2 or 3, CX CAN:
   Cervical cancer.

2.4. Altered Metabolites in Patients with CINs and Cervical Cancer

   A total of 28 significantly altered metabolites were identified in the
   positive mode (19 metabolites) and negative mode (16 metabolites)
   (Kruskal–Wallis test p < 0.05). For global visualization of the
   significantly altered metabolites, hierarchical cluster analysis (HCA)
   and area under the curve (AUC) analysis were performed. A dendrogram
   produced through the HCA revealed three distinct clusters in both the
   positive and negative modes ([73]Figure S3). Metabolites showing
   significant trends in plasma levels among the normal, CIN, and cervical
   cancer groups were identified as potential biomarkers for early
   detection of cervical cancer. Of these, we chose the top seven
   metabolites, namely AMP, aspartate, glutamate, hypoxanthine, lactate,
   proline, and pyroglutamate, on the basis of the HCA clusters and AUC
   analysis ([74]Table S2).

2.5. Validation and Diagnostic Performance of Selected Metabolites

   The levels of the selected metabolites in a group of participants
   comprising 69 normal, 55 CIN1, 42 CIN2/3, and 60 cervical cancer
   patients ([75]Table 1) were measured using UPLC-triple-quadrupole
   (TQ)-MS and analyzed in the validation phase by Bonfferoni multiple
   comparisons test. [76]Figure 3 depicts the boxplots of their
   concentrations in the CIN, cervical cancer, and normal cases. All of
   the metabolites were up-regulated in CIN and cervical cancer, except
   for proline metabolite in the CIN1 group. Interestingly, AMP was
   significantly elevated in the cervical cancer group as compared to CIN
   and normal, whereas no significant difference was observed for proline
   between CIN and cervical cancer as compared to normal (p < 0.05).

Figure 3.

   [77]Figure 3
   [78]Open in a new tab

   Boxplots of metabolites between normal and cervical cancer. Post-hoc
   multiple comparisons using Bonfferoni method was performed for
   significant difference. * p < 0.05, ** p < 0.01, *** p < 0.001. CIN1:
   Cervical intraepithelial neoplasia 1, CIN2/3: Cervical intraepithelial
   neoplasia 2 or 3, CX CAN: Cervical cancer.

   [79]Table 2 shows the groups’ AUC values of targeted metabolites
   obtained through multiple comparison analysis. The AUC values indicate
   the diagnostic potentials of the metabolites as unique biomarkers for
   identification of CINs and cervical cancers. The AUC value (0.80) of
   aspartate metabolite indicated a clear discrimination between the
   normal and CIN2/3 groups, whereas, those of glutamate and hypoxanthine
   metabolites clearly discriminated between normal and cervical cancer
   and CIN1 and cervical cancer, respectively. We thereafter combined the
   metabolites into various sets and subjected them to AUC analysis to
   evaluate their diagnostic performances as combined biomarkers for CINs
   and cervical cancers. The AUC value of three combined metabolites
   (aspartate, glutamate, and hypoxanthine) was 0.81 between the normal
   and CIN2/3 group and the normal and cervical cancer group; the AUC
   values of seven combined metabolites (AMP, aspartate, glutamate,
   hypoxanthine, lactate, proline, and pyroglutamate) was 0.82 between the
   normal and CIN2/3 group and 0.83 between the normal and cervical cancer
   group.

Table 2.

   Area under the curves (AUCs) among normal, CINs, and cervical cancers.
   Metabolite N vs. CIN2/3 N vs. CX CAN CIN1 vs. CIN2/3 CIN1 vs. CX CAN
   N+CIN1 vs. CIN2/3 + CX CAN
   AMP 0.53 0.71 <0.50 0.68 0.62
   Aspartate 0.80 0.76 0.71 0.67 0.74
   Glutamate 0.76 0.81 0.69 0.73 0.76
   Hypoxanthine 0.68 0.77 0.72 0.80 0.74
   Lactate 0.74 0.74 0.67 0.69 0.71
   Proline 0.68 0.71 0.51 0.54 0.62
   Pyroglutamate 0.72 0.74 0.68 0.69 0.71
   Aspartate,Glutamate 0.80 0.81 0.70 0.71 0.76
   Aspartate,Hypoxanthine 0.82 0.76 0.73 0.73 0.73
   Glutamate,Hypoxanthine 0.76 0.81 0.67 0.76 0.76
   Aspartate,Glutamate,Hypoxanthine 0.81 0.81 0.73 0.74 0.75
   AMP,Aspartate,Glutamate,Hypoxanthine 0.82 0.82 0.71 0.73 0.75
   Aspartate,Glutamate,Hypoxanthine,Lactate 0.80 0.80 0.73 0.73 0.76
   Aspartate,Glutamate,Hypoxanthine,Proline 0.82 0.81 0.72 0.72 0.75
   Aspartate,Glutamate,Hypoxanthine,Pyroglutamate 0.80 0.81 0.74 0.76 0.76
   AMP,Aspartate,Glutamate,Hypoxanthine,Lactate 0.81 0.82 0.71 0.73 0.75
   AMP,Aspartate,Glutamate,Hypoxanthine,Proline 0.82 0.83 0.72 0.72 0.75
   AMP,Aspartate,Glutamate,Hypoxanthine,Pyroglutamate 0.81 0.82 0.73 0.77
   0.78
   Aspartate,Glutamate,Hypoxanthine,Lactate,Proline 0.82 0.81 0.72 0.72
   0.75
   Aspartate,Glutamate,Hypoxanthine,Lactate,Pyroglutamate 0.81 0.80 0.74
   0.76 0.77
   AMP,Aspartate,Glutamate,Hypoxanthine,Lactate,Proline 0.82 0.83 0.71
   0.72 0.75
   AMP,Aspartate,Glutamate,Hypoxanthine,Lactate,Pyroglutamate 0.81 0.82
   0.72 0.78 0.78
   Aspartate,Glutamate,Hypoxanthine,Lactate,Proline,Pyroglutamate 0.82
   0.82 0.73 0.77 0.78
   AMP,Aspartate,Glutamate,Hypoxanthine,Lactate,Proline,Pyroglutamate 0.82
   0.83 0.72 0.78 0.78
   [80]Open in a new tab

   AMP: Adenosine monophosphate, N: Normal, CIN1: Cervical intraepithelial
   neoplasia 1, CIN2/3: Cervical intraepithelial neoplasia 2 or 3, CX CAN:
   Cervical cancer.

2.6. Association of Metabolites with CINs and Cervical Cancer Risk

   [81]Table 3 displays the associations of targeted metabolites with
   CIN2/3 or cervical cancer risk by multivariate logistic analysis. As
   can be seen, most of the metabolites were significantly associated with
   increased risk of developing CIN2/3 and cervical cancer from normal.
   Aspartate, moreover, was significantly associated with increased risk
   of developing cervical cancer from normal. Note also that a high level
   of aspartate was shown to have increased risk of developing CIN2/3
   compared with CIN1 (OR, 8.92; CI, 2.38–33.4) and CIN2/3 from normal
   (OR, 4.31; CI, 1.38–13.5). For glutamate, the odds ratios (ORs) and
   correspondent confidence intervals (CIs) for the risk of developing
   CIN2/3 and cervical cancer from normal were 7.99 (2.31–27.6) and 6.31
   (1.74–22.9), respectively. Hypoxanthine also showed an increased risk
   of developing CIN2/3 compared with CIN1 (OR, 7.90; CI, 2.00–31.3). The
   elevated level of pyroglutamate was associated with higher risks of
   developing cervical cancer (OR range 2.94–8.02). All of the metabolites
   at the elevated level showed high risks (OR range 2.12–4.49) of
   developing CIN2/3 + cervical cancer from normal + CIN1. These results
   indicated that higher levels of metabolites were significantly
   associated with increased risks of developing CIN2/3 and cervical
   cancer.

Table 3.

   Associations of targeted metabolites with CIN2/3 and cervical cancer
   risk.
   Metabolite N CIN2/3 N vs. CIN2/3 CX CAN N vs. CX CAN CIN1 CIN23 CIN1
   vs. CIN2/3 CX CAN CIN1 vs. CX CAN N + CIN1 CIN23 + CX CAN N + CIN1 vs.
   CIN23 + CX CAN
   n = 69 n = 42 mOR (95% CI) n = 60 mOR (95% CI) n = 55 n = 42 mOR (95%
   CI) n = 60 mOR (95% CI) n = 124 n = 102 mOR (95% CI)
   AMP
   Low 49.3 38.1 ref 25 ref 40.1 35.7 ref 25 ref 50 30.4 ref
   High 50.7 61.9 2.11 (0.79–5.63) 75 1.52 (0.54–4.28) 50.9 64.3 4.34
   (1.29–14.6) 75 0.62 (0.12–3.28) 50 69.6 2.34 (1.13–4.83)
   Aspartate
   Low 50.7 11.9 ref 21.7 ref 49.1 14.3 ref 25 ref 50 20.6 ref
   High 49.3 88.1 4.31 (1.38–13.5) 78.3 1.64 (0.59–4.58) 50.9 85.7 8.92
   (2.38–33.4) 75 0.81 (0.15–4.24) 50 79.4 2.80 (1.36–5.78)
   Glutamate
   Low 50.7 11.9 ref 8.3 ref 50.9 28.6 ref 20 ref 50.8 14.7 ref
   High 49.3 88.1 7.99 (2.31–27.6) 91.7 6.31 (1.74–22.9) 49.1 71.4 2.68
   (0.87–8.20) 80 0.60 (0.10–3.71) 49.2 85.3 4.49 (2.05–9.82)
   Hypoxanthine
   Low 49.3 23.8 ref 15 ref 50.9 14.3 ref 8.3 ref 50.8 17.7 ref
   High 50.7 76.2 5.30 (1.64–17.1) 85 4.33 (1.30–14.5) 49.1 85.7 7.90
   (2.00–31.3) 91.7 2.36 (0.42–13.3) 49.2 32.3 4.26 (1.95–9.28)
   Lactate
   Low 50.7 14.3 ref 13.3 ref 50.9 33.3 ref 25 ref 50 20.6 ref
   High 49.3 85.7 5.18 (1.74–15.4) 86.7 3.93 (1.24–12.5) 49.1 66.7 1.98
   (0.68–5.78) 75 0.96 (0.17–5.50) 50 79.4 2.30 (1.13–4.69)
   Proline
   Low 50.7 23.8 ref 20 ref 50.9 54.8 ref 53.3 ref 50 30.4 ref
   High 49.3 76.2 5.13 (1.71–15.4) 80 5.23 (1.50–18.3) 49.1 45.2 1.04
   (0.35–3.09) 46.7 0.69 (0.12–3.94) 50 69.6 2.12 (1.02–4.43)
   Pyroglutamate
   Low 50.7 23.8 ref 10 ref 49.1 23.8 ref 11.7 ref 50.8 16.7 ref
   High 49.3 76.2 2.94 (1.10–7.84) 90 8.02 (2.13–30.3) 50.9 76.2 5.46
   (1.63–18.4) 88.3 4.54 (0.65–32.0) 49.2 83.3 4.14 (1.92–8.92)
   [82]Open in a new tab

   mOR: Multivariate odds ratio, CI: Confidence interval, ref: Reference,
   AMP: Adenosine monophosphate, N: Normal, CIN1: Cervical intraepithelial
   neoplasia 1, CIN2/3: Cervical intraepithelial neoplasia 2 or 3, CX CAN:
   Cervical cancer. Adjusted for age, BMI, marital status, education,
   menopause status, parity, smoking status.

2.7. Combined Effects of Targeted Metabolites with HPV Status

   [83]Table 4 displays the combined effects of HPV infection with
   targeted metabolites on the development risks of CIN2/3 and cervical
   cancer. As expected, high levels of targeted metabolites with positive
   HPV status showed a significant risk for development of CIN2/3 and
   cervical cancer. At the elevated level, proline showed the highest risk
   (OR 66.9; p for trend = 0.0001) of developing CIN2/3 from CIN1,
   followed by hypoxanthine, glutamate, and lactate (OR range 16.54–33.48;
   p for trend < 0.01). Similarly, proline, pyroglutamate, lactate, and
   glutamate at the elevated level also demonstrated increased risk for
   developing cervical cancer from normal (OR range 24.52–34.22; p for
   trend < 0.0009). These results indicated that the risk of developing
   CIN2/3 or cervical cancer is significantly higher if patients are
   positive for HPV infection.

Table 4.

   Combined effects of metabolites with human papillomavirus (HPV) status
   on CIN2/3 and cervical cancer risk.
   Metabolite HPV N vs. CIN2/3 N vs. CX CAN CIN1 vs. CIN2/3 CIN1 vs. CX
   CAN N+CIN1 vs. CIN2/3 + CX CAN
   mOR (95% CI) mOR (95% CI) mOR (95% CI) mOR (95% CI) mOR (95% CI)
   AMP
   Low Neg ref ref ref ref ref
   Low Pos 6.56 (0.99–43.2) 5.36 (0.99–29.0) 5.32 (0.79–35.9) 5.31
   (0.98–28.8) 3.36 (1.02–11.1)
   High Neg 1.90 (0.27–13.3) 0.87 (0.12–6.06) 1.82 (0.26–12.8) 0.85
   (0.12–5.94) 1.24 (0.32–4.82)
   High Pos 12.19 (2.21–67.1) 8.54 (1.63–44.8) 12.78 (2.31–70.7) 8.46
   (1.61–44.6) 9.33 (2.94–29.6)
   p interaction ^1 0.0022 0.0184 0.0008 0.0184 <0.0001
   p trend ^2 0.0065 0.0489 0.0034 0.0513 0.0002
   Aspartate
   Low Neg ref ref ref ref ref
   Low Pos 2.52 (0.31–20.5) 3.58 (0.67–19.2) 3.45 (0.47–25.3) 4.67
   (0.93–23.4) 2.47 (0.73–8.34)
   High Neg 1.64 (0.24–11.2) 0.46 (0.06–3.46) 1.74 (0.25–12.0) 0.50
   (0.07–3.76) 1.08 (0.28–4.13)
   High Pos 13.93 (2.62–74.0) 7.71 (1.60–37.1) 13.52 (2.55–71.7) 6.97
   (1.45–33.4) 9.15 (2.96–28.3)
   p interaction <0.0001 0.0067 0.0002 0.0236 <0.0001
   p trend 0.0009 0.041 0.0013 0.0802 <0.0001
   Glutamate
   Low Neg ref ref ref ref ref
   Low Pos 0.98 (0.11–9.18) 1.14 (0.11–12.3) 3.62 (0.69–18.9) 3.28
   (0.60–18.0) 2.76 (0.68–11.3)
   High Neg 1.49 (0.20–11.3) 1.33 (0.16–10.7) 1.51 (0.21–10.6) 0.92
   (0.12–7.06) 2.29 (0.56–9.40)
   High Pos 27.18 (4.23–175) 24.52 (3.61–167) 17.01 (3.38–85.6) 14.33
   (2.64–77.8) 14.85 (4.27–51.7)
   p interaction <0.0001 <0.0001 0.0002 0.001 <0.0001
   p trend <0.0001 0.0005 0.0006 0.0047 <0.0001
   Hypoxanthine
   Low Neg ref ref ref ref ref
   Low Pos 7.59 (0.93–61.9) 3.35 (0.45–24.9) 46.45 (2.11–999) 10.83
   (0.73–161) 2.11 (0.56–8.04)
   High Neg 5.25 (0.61–45.3) 1.82 (0.24–13.6) 9.76 (0.70–135) 1.27
   (0.18–9.21) 1.58 (0.42–6.05)
   High Pos 21.89 (3.36–142) 14.88 (2.57–86.2) 33.48 (2.83–396) 8.19
   (1.44–46.6) 11.18 (3.55–35.2)
   p interaction 0.0005 0.0009 0.003 0.0076 <0.0001
   p trend 0.0006 0.0023 0.0024 0.0216 <0.0001
   Lactate
   Low Neg ref ref ref ref ref
   Low Pos 2.33 (0.31–17.8) 5.35 (0.69–41.7) 10.40 (1.80–60.0) 3.30
   (0.71–15.4) 4.24 (1.23–14.6)
   High Neg 1.86 (0.27–12.8) 2.79 (0.36–21.4) 3.57 (0.50–25.3) 0.34
   (0.03–3.8) 1.63 (0.42–6.36)
   High Pos 17.99 (3.31–97.8) 26.84 (3.69–196) 16.54 (2.86–95.7) 12.01
   (2.35–61.3) 9.93 (3.05–32.4)
   p interaction <0.0001 0.0006 0.0105 0.0022 <0.0001
   p trend 0.0002 0.0013 0.0058 0.0139 0.0003
   Proline ^3
   Low Neg ref ref ref ref ref
   Low Pos – 4.16 (0.52–33.5) 6.61 (1.53–28.6) 4.83 (1.07–21.8) 7.65
   (1.95–29.9)
   High Neg – 2.79 (0.35–22.2) 4.29 (0.63–29.2) 1.38 (0.18–10.7) 3.10
   (0.73–13.2)
   High Pos - 34.22 (3.96–296) 66.90 (8.82–507) 22.04 (3.60–135) 13.46
   (3.51–51.7)
   p interaction - 0.0003 0.0004 0.0028 <.0001
   p trend - 0.0009 0.0001 0.0029 <.0001
   Pyroglutamate
   Low Neg ref ref ref ref ref
   Low Pos 6.76 (0.92–49.9) 3.81 (0.39–37.7) 6.90 (0.94–50.5) 6.71
   (0.75–60.2) 4.23 (1.07–16.8)
   High Neg 2.68 (0.34–21.0) 4.59 (0.56–37.4) 2.83 (0.36–22.0) 4.76
   (0.60–37.7) 3.15 (0.77–12.9)
   High Pos 15.50 (2.79–86.0) 27.93 (4.03–194) 15.79 (2.86–87.1) 22.76
   (3.47–149) 16.17 (4.55–57.5)
   p interaction 0.001 0.0004 0.001 0.0013 <0.0001
   p trend 0.0019 0.0005 0.0017 0.0011 <0.0001
   [84]Open in a new tab

   CI: Confidence interval; AMP: Adenosine monophosphate; N: Normal, CIN1:
   Cervical intraepithelial neoplasia 1, CIN2/3: Cervical intraepithelial
   neoplasia 2 or 3, CX CAN: Cervical cancer, POS: Positive; NEG:
   Negative. Adjusted for age, BMI, marital status, education, menopause
   status, parity, smoking status. ^1 P is for the interaction provided by
   the logistic regression for multiplicative terms (a × b). ^2 P is for
   the trend according to the order of combination provided by the
   logistic regression analysis. ^3 The analysis of proline in N vs.
   CIN2/3 groups was not possible because of the small sample size.

2.8. Pathway Analysis for Quantitative Metabolites

   Metabolomic pathway analysis was performed for the seven targeted
   metabolites using the MetPA web-based tool. The highest impact value
   was obtained for the alanine, aspartate, and glutamate metabolic
   pathways; these results were consistent with the global pathway
   analysis ([85]Figures S4 and S5; impact value > 0.4, FDR p-value <
   0.05). Metabolic pathways including arginine and proline metabolism,
   pyruvate metabolism, aminoacyl-tRNA biosynthesis, and D-glutamine and
   D-glutamate metabolism were statistically related (impact > 0.1, FDR
   p-value < 0.05).

3. Discussion

   Alteration in metabolic pathways has been involved in the development
   of neoplasia and is characterized as a hallmark of cancer [[86]23]. A
   growing body of evidence shows that malignant transformation requires
   large amounts of energy to sustain high rates of cell proliferation and
   energy consumption, which leads to alterations of key metabolic
   pathways [[87]24,[88]25].

   In this study, we used global and targeted metabolomic profiling to
   identify plasma metabolite biomarkers for non-invasive early detection
   of cervical cancer and as risk factors for cancer development. Cervical
   cancer causes changes in body metabolism, and results in alterations in
   circulating metabolites. Thus, investigation of
   cervical-cancer-associated metabolic pathways can provide important
   insights into the different mechanisms adopted during the development
   and progression of tumors and proposes new methods for its early
   detection. Our findings showed that 28 metabolites among normal, CINs,
   and cervical cancers were significantly altered in the global
   metabolite profiling (discovery phase). These metabolites were
   successfully mapped to 37 normal and cervical cancer, 29 CIN1 and
   cervical cancer, and 33 normal + CIN1 and CIN2/3 + cervical cancer
   pathways. The results obtained from our pathway analysis indicated that
   the metabolic pathways of alanine, aspartate, and glutamate metabolism,
   arginine and proline metabolism, taurine and hypotaurine metabolism,
   and pyruvate metabolism were significantly altered between cancer
   patients and the normal group. In the validation phase, seven
   metabolites that were statistically different in cervical
   carcinogenesis progression were selected as potential biomarkers on the
   basis of AUC (>0.80) and HCA analyses. Furthermore, HPV, a known causal
   agent of cervical cancer development, was shown to represent high risk
   when combined with targeted altered metabolites in the validation
   phase.

   Endogenous and exogenous amino acids are considered to be vital sources
   of nutrients that are distributed all over the body to contribute to
   metabolism, gene expression, cell multiplications, and inflammatory
   reactions. The speedy proliferation and increased metabolism of tumor
   cells require amino acids for protein and nucleic acids synthesis
   [[89]26]. It has already been reported that amino acids such as serine,
   glutamine, aspartic acid, and proline metabolism play an important role
   in cancer progression and contribute to tumor metabolic reprogramming
   [[90]27]. The analysis of sera from cancer patients often shows altered
   amino acid profiles compared with healthy patients [[91]28,[92]29].
   Hasim, Aili, Maimaiti, Abudula, and Upur [[93]21] showed that the total
   serum concentrations of aspartate, proline, and glutamate, among
   others, were gradually decreased from CIN to invasive cancer. In
   another study, the concentration of L-lysine essential amino acid also
   was decreased in cervical cancer patients [[94]30]. Ye et al. [[95]31]
   reported that metabolomics profile of serum samples obtained from
   cervical cancer patients exhibited reduced levels of most essential and
   non-essential amino acids, such as isoleucine, valine, tyrosine, and
   glycine, among others as compared to cervicitis and CIN.
   Correspondingly, we found altered amino acids profiles in cervical
   cancer patients compared with normal patients. By contrast, we found
   elevated levels of plasma aspartate, glutamate, proline, and
   pyroglutamate in cervical cancer patients compared with those of normal
   individuals ([96]Figure 3). It is an established fact that metabolism,
   including that of amino acids, changes not only in cancerous cells, but
   also in plasma [[97]32,[98]33]. However, there are inconsistencies
   among altered amino acid profiles of cancer patients [[99]21,[100]34]
   as well as discrepancies between the reported literature and our
   present results. Differences among the findings of various studies can
   be attributed to study design, detection procedures, pre-cancerous
   lesion status, and sample size. However, findings similar to ours also
   are available. For example, some studies have reported elevated levels
   of amino acid profiles in cancerous patients [[101]35,[102]36].
   Augmentation of amino acids in the plasma of cancer patients indicates
   the utilization of amino acids for protein synthesis required for
   increased cell growth [[103]27]. These results suggest that cancer
   progression as characterized by the severity of invasion and metastasis
   is highly associated with plasma amino acid profiles [[104]37].
   However, it remains unclear how metabolic changes occurring in cancer
   patients influence plasma amino-acid profiles systemically.

   Lactate metabolite is normally found in elevated levels in tumor
   hypoxia, a condition in which cells acquire energy through glycolysis
   instead of oxidative phosphorylation [[105]38]. We found significantly
   elevated levels of lactate in CIN and cervical cancer cases compared
   with normal individuals. In cervical cancer patients relative to CIN2/3
   patients, the lactate metabolites levels were slightly reduced. Several
   studies have reported similar findings of elevated levels of lactate in
   other types of cancer [[106]39,[107]40]. In contrast to our findings,
   Hasim et al. [[108]41] reported reduced levels of lactate in CIN and
   cervical cancer as compared with normal individuals. The discrepancy in
   these results could have been due to differences in study design and
   detection methods.

   Notably, we observed that metabolites from the purine metabolic
   pathway, AMP, and hypoxanthine were associated with cervical cancer
   risk. During progression from normal to CIN1, the level of hypoxanthine
   reduced and then increased in CIN2/3 and cervical cancer when compared
   to normal individuals. Hypoxanthine has been described as a protective
   agent against hypoxic injury and cytotoxic compounds in normal and
   cancerous cells [[109]42]. It is oxidized to xanthine by xanthine
   oxidase (a form of xanthine oxidoreductase), whereas uric acid is the
   end product of their degradation [[110]43]. Previous studies have
   suggested that uric acid might contribute to cancer risk, recurrence,
   and mortality [[111]44], which conclusions point to the importance of
   this metabolic pathway in influencing cancer progression and outcomes.
   AMP plays important roles in the regulation of cell proliferation and
   in acting as an intermediate in purine metabolism. Elevated levels of
   AMP can be attributed to increased conversion of adenosine to AMP by
   adenosine kinase enzymes during cell proliferation [[112]45]. In
   agreement with our findings, Satoh et al. [[113]46] applied a
   multiomics approach to paired normal and tumor tissue obtained from 275
   colorectal cancer patients and found that almost all metabolic genes of
   the de novo purine/pyrimidine synthesis pathway were up-regulated.
   Consistent with our findings, Sahu et al. [[114]47] performed
   metabolomic analysis in urothelial carcinoma and found elevated levels
   of both purine and pyrimidine metabolites.

   Although a cancerous tumor is limited to a certain organ, changes in
   metabolite levels are influenced by their metabolism in, and excretion
   from, several organs of the body. Therefore, the blood plasma of cancer
   patients contains almost all of the metabolite information related to
   pathogenic alterations triggered by disease, which data reveal are
   abnormal alterations at the gene expression and regulation levels as
   well as aberrations in the functions of multiple organs and tissues.
   However, as already noted in these pages, it remains unclear how
   changes in metabolic profiles occurring in pre-cancerous and cancerous
   patients systematically affect plasma metabolites, including amino
   acid.

   Previous epidemiological studies have shown associations between
   various metabolites and cervical cancer risk; for example, levels of
   serum triglyceride and systolic blood pressure were positively
   associated with cervical cancer risk [[115]48,[116]49,[117]50,[118]51].
   Walker, Burrell, Flatley, and Powers [[119]8] performed metabolomic
   analysis and suggested that metabolite profiling could be used to
   rapidly identify women at increased risk of cervical cancer. Likewise,
   we also found substantial associations between targeted metabolites and
   CIN2/3 or cervical cancer risk ([120]Table 3). These results indicate
   an increased risk of developing CIN2/3 and cervical cancer from a
   normal group having elevated levels of these metabolites in plasma.

   Nearly all cases of cervical cancer can be attributed to HPV infection
   [[121]52]. We have found 78.3% HPV positive cases for cervical cancer
   group ([122]Table 1), and the results are in agreement with Lei et al.
   [[123]53], who studied HPV infection in cervical cancer in a nationwide
   cohort 2002–2011 and found 80.6% were positive for HPV infection. In
   another study, Hooi et al. [[124]54] investigated the prevalence of HPV
   genotypes in cervical cancers and CIN in Curaçao and found 88.5% cases
   were HPV positive. However, HPV status in a cervical tumor may be
   dependent on various factors such as type of test performed, sample
   quality, laboratory conditions, and sample size. HPV plays an important
   role in the progression of precursor lesions and cervical cancers. HPV
   is essential along with cofactors that aid viral persistence and
   progression [[125]55]. We have discovered in our current study that HPV
   infection combined with elevate levels of metabolites substantially
   increased the risk of cancer progression. Metabolites such as
   aspartate, glutamate, hypoxanthine, and proline showed an increased
   risk for the development of CIN2/3 and cervical cancer from normal with
   highest risk was observed for proline (OR, 66.90; CI, 8.82–507).

   Although our approach to establish a screening strategy for cervical
   cancer and precancerous lesions, plasma metabolomic analysis, and the
   search for unique metabolic pathways is novel and promising, there were
   some limitations to this study. First, all of the samples collected
   were non-fasting ones, which, in fact, potentially increased
   variability among our subjects. Second, the sample size was small,
   which could have affected the robustness of our statistical analysis.
   Third, we performed UPLC-QTOF-MS analysis, which might not be an
   appropriate screening tool for large populations, due to its high cost
   in developing countries. Fourth, personal and demographic factors such
   as sex, age, BMI, diet, and smoking status might influence the
   metabolomic landscape. Further study is needed to optimize and validate
   cost-effective methods for detection of plasma metabolic biomarkers.
   Additionally, careful matching of known risk factors and adjustment of
   covariates in statistical analysis could be considered in order to
   avoid false positivity. Nevertheless, global profiling of plasma
   metabolites with targeted validation and comprehensive collection of
   epidemiological information within a proper hospital setup could offer
   important resources for identifying unique metabolic biomarkers
   suitable for clinical application.

4. Materials and Methods

4.1. Study Population and Sample Collection

   This study included subjects aged 18 to 65 years who had participated
   in the Korean Prospective Study of the Transition of Human
   Papillomavirus into Cervical Carcinoma since 2006. These women were
   randomly selected from the six gynecology and oncology departments
   located in South Korean university hospitals. Details on the study’s
   design criteria are available in our previous paper [[126]56]. All of
   the patients had been diagnosed with CINs or cervical cancers
   histologically. Blood samples were collected from the participants in a
   non-fasting state and centrifuged at 3000 rpm for 20 min at 4 °C; the
   obtained plasma was stored at −80 °C until further analysis. A total of
   205 samples (70 normal, 54 CIN1, 27 CIN2/3, 54 cervical cancer) were
   selected for positive-mode global analysis, and 202 (67 normal, 54
   CIN1, 27 CIN2/3, 54 cervical cancer) for negative-mode global analysis.
   Additionally, a total of 226 plasma samples (69 normal, 55 CIN1, 42
   CIN2/3, 60 cervical cancer) were selected for targeted analysis.
   According to the requirements of the National Cancer Center (NCC)’s
   Institutional Review Board, written consent was obtained from all of
   the participants. This study was approved by the NCC’s Institutional
   Ethics Committee (IRB No. NCC2016-0147).

4.2. Global Metabolite Profiling Using UPLC-QTOF-MS

   A UPLC-QTOF-MS-based platform was used to analyze polar metabolites
   from chloroform/methanol extract. Briefly, plasma samples (50 μL) were
   extracted using a 500 μL chloroform:methanol (2:1, v/v) solution and
   100 μL water. The aqueous supernatant was vacuum dried and
   reconstituted into 250 μL water:acetonitrile (4:1, v/v) solution.
   Finally, 5 μL solution was injected into the UPLC-QTOF-MS system. The
   UPLC-QTOF-MS analysis was performed using the ACQUITY UPLC system
   (Waters, Milford, MA, USA) coupled with a triple TOF™ 5600 mass
   spectrometer equipped with an electrospray ionization (ESI) source (AB
   Sciex, Concord, ON, Canada). Chromatographic separation was carried out
   at 40 °C on an Acquity UPLC HSS T3 column (2.1 mm × 100 mm, 1.7 μm;
   Waters) with a binary gradient at a flow rate of 0.4 mL/min. The mobile
   phases consisted of 0.1% formic acid in water (solvent A) and 0.1%
   formic acid in acetonitrile (solvent B). The steps of the gradient
   profile used to equilibrate the initial gradient for subsequent runs
   were as follows: 1% B, 1%–10% B from 0–3 min, 10%–30% B from 3–5 min,
   30%–50% B from 5–10 min, 50%–70% B from 10–13 min, 70%–90% B from 13–15
   min, 90%–1% B from 15–18 min, 1% B from 18–20 min.

   The mass spectrometer was operated in both the positive and negative
   ion modes, and data were acquired in the 50–1000 m/z mass range. Total
   ion chromatograms were acquired according to the following operation
   parameters: Capillary voltages of +5500 V and −4500 V for the positive
   and negative modes, a nebulizer pressure of 60 psi, a drying gas
   pressure of 60 psi, a curtain gas pressure of 30 psi, a source
   temperature of 500 °C, a declustering potential of ± 90 eV, a collision
   energy of ± 10 eV for single MS, and a collision energy of ± 35 eV for
   MS/MS. Data from the MS/MS analyses were acquired by automatic
   fragmentation, in which process the five most intense mass peaks were
   fragmented. Mass accuracy was maintained by use of an automated
   calibrant delivery system interfaced with the second inlet of the
   DuoSpray source. Equal amounts of all of the samples were pooled to
   generate QC samples, which were analyzed prior to sample acquisition
   and after every 15 samples in order to monitor the stability and
   reproducibility of the analytical system.

4.3. Metabolite Quantification Using UPLC-TQ-MS

   Modern TQ-MS provides the ability to detect and quantify a large number
   of metabolites. The present quantification of targeted metabolites was
   performed on Agilent 1290 Infinity II LC and Agilent 6495 Triple
   Quadrupole MS systems equipped with an Agilent Jet Stream ESI source
   (Agilent Technologies, Palo Alto, CA, USA). MassHunter Workstation (Ver
   B.06.00, Agilent Technologies) software was used for data acquisition
   and analysis. Chromatographic separation was performed using a Scherzo
   SM-C18 column (2 mm × 100 mm, 3 μm; Imtakt, Kyoto, Japan). The flow
   rate and injection volume were set at 0.35 mL/min and 1 μL,
   respectively. Mobile phase A consisted of 0.1% formic acid in water,
   and phase B consisted of 0.1% formic acid in methanol. The linear
   gradient used for elution and equilibration of the initial gradient for
   subsequent runs was as follows: 5% B from 0–3 min, 5–90% B from 3–10
   min, 90% B from 10–12 min, 5–95% B from 12–13 min, 5% B from 13–15 min.
   Quantification was performed in the multiple reaction monitoring (MRM)
   mode, and the optimal conditions for each metabolite were determined by
   flow injection of individual standards (100 ng/mL in 20% methanol) into
   the ESI source in the positive and negative ion modes.
   ^13C[6]-Phenylalanine was used as an internal standard. The compound
   retention times and MRM transitions are summarized in [127]Table S1.

4.4. Statistical Analysis

   A multivariate statistical analysis was performed using SIMCA-P+
   version 12.0 (Umetrics, Umeå, Sweden). PCA using an unsupervised method
   was applied to obtain an overview of the metabolic data. All of the
   metabolite variables obtained from the UPLC-QTOF-MS datasets were
   scaled to unit-variances prior to conducting the PCA. The model
   validity was evaluated from model parameters R^2 and Q^2, which provide
   information for the interpretability and predictability, respectively,
   of the model. The differences between groups were compared using
   Kruskal–Wallis test for continuous variables and chi-square test for
   categorical variables. The metabolites obtained from global analysis
   were subjected to Kruskal–Wallis analysis, HCA, and AUC analysis
   [[128]57,[129]58]. Afterwards, seven metabolites were selected on the
   basis of the HCA and AUC values. Post-hoc multiple comparisons using
   the Bonfferoni method were applied to identify significant inter-group
   differences in the targeted metabolites [[130]59]. An AUC analysis also
   was performed on the targeted metabolites. Metabolite enrichment was
   log transformed before being subjected to pathway analysis in
   MetaboAnalyst version 4.0 (Xia Lab, McGill University, Montreal, QC,
   Canada) [[131]60]. Unconditional logistic regression was conducted to
   estimate the ORs and the correspondent CIs (95%), which were adjusted
   for age, BMI, marital status, education, menopause status, pregnancy,
   and smoking status. To categorize the metabolites, the median value in
   the normal group was calculated and used as the cutoff point for
   dichotomization into high- and low-risk groups. The combined effects of
   HPV status with metabolites were calculated using unconditional
   logistic regression analysis, and the estimated ORs and CIs (95%) after
   the variables were log2-transformed. All of the statistical tests were
   2-sided, with the significance set at p < 0.05. All of the other
   statistical analyses and visualizations were performed using the
   ggplot2 packages in the R platform [[132]58,[133]61]. The overall
   scheme of the metabolomic analysis for the discovery of novel
   biomarkers for cervical carcinogenesis is presented in [134]Figure 4.

Figure 4.

   [135]Figure 4
   [136]Open in a new tab

   Schematic flow chart of metabolomic analysis in present study.
   UPLC-QTOF-MS: Ultra-performance liquid chromatography-quadrupole
   time-of-flight mass spectrometry; UPLC-TQ-MS: Ultra-performance lipid
   chromatography-triple quadruple mass spectrometry; AUC: Area under the
   curve; HCA: Hierarchical cluster analysis.

5. Conclusions

   The use of metabolomics for detection of cervical cancer biomarkers has
   been shown by previous studies as well as the present one to be quite
   promising. The results obtained in the current study showed that 28
   metabolites were significantly altered among normal controls, CINs, and
   cervical cancers in the global profiling phase. We determined that
   analysis of circulating AMP, aspartate, glutamate, hypoxanthine,
   lactate, proline, and pyroglutamate levels have the potential to
   distinguish patients with CINs and cervical cancer from normal
   (healthy) patients. The risk of developing cervical cancer was high in
   cases where the metabolite levels were elevated. The risk of developing
   precancerous lesions and invasive cancer was even higher where patients
   had elevated levels of metabolites and showed positivity for HPV
   infection. Further studies are necessary in order to fully understand
   the association of metabolic changes with cervical cancer progression
   and their diagnostic potentials.

Abbreviations

   AMP Adenosine monophosphate
   AUC Area under the curve
   BMI Body mass index
   CI Confidence interval
   CIN Cervical intraepithelial neoplasia
   CV Coefficients of variation
   FDR False discovery rate
   HCA Hierarchical cluster analysis
   HPV Human papillomavirus
   OR Odds ratio
   PCA Principal component analysis
   QC Quality control
   UPLC-QTOF-MS Ultra-performance liquid
   chromatography-quadrupole-time-of-flight mass spectrometry
   UPLC-TQ-MS Ultra-performance liquid chromatography-triple-quadrupole
   mass spectrometry
   [137]Open in a new tab

Supplementary Materials

   The following are available online at
   [138]https://www.mdpi.com/2072-6694/11/4/511/s1, Figure S1: PCA score
   plots with QC clusters, Figure S2: Construction of the altered
   metabolism pathways using MetPA, Figure S3: HCA results (a. positive,
   b. negative mode), Figure S4: Construction of altered metabolic
   pathways using MetPA analysis for targeted metabolites, Figure S5:
   Alanine, aspartate, and glutamate metabolism ([139]C00049: Asparate,
   [140]C00025: Glutamate). Table S1: Retention times and multiple
   reaction monitoring transitions of plasma metabolites quantified by
   UPLC-TQ-MS, Table S2: AUC values of significantly altered metabolites
   (p value < 0.05) in given HCA clusters.
   [141]Click here for additional data file.^ (841.1KB, pdf)

Author Contributions

   All of the authors contributed significantly to this work. M.K.K.,
   M.K., G.-S.H., and S.-s.S. designed the study; S.-s.S. enrolled the
   subjects; G.-S.H. supervised metabolomic research; M.N. carried out
   metabolomic experiments and analyzed the data. S.J. and J.S.H.
   contributed to the experimental work; M.K. analyzed the results; I.K.
   drafted the paper. All of the authors read and approved the final
   manuscript.

Funding

   The current work is supported by the National Cancer Center (NCC),
   South Korea (grant Nos. NCC-1610210 and NCC-1810920). This study was
   supported by the National Research Foundation (NRF) of Korea (No.
   2017M3A9C4065961 and NRF-2017M3A9D5A01052449) and the Korea Basic
   Science Institute ([142]T39623 and [143]T39720).

Conflicts of Interest

   The authors declare no conflict of interest.

References