1.   Introduction

Bladder cancer (BC) is the second most frequent genitourinary malignancy ^[1]. Non-muscle invasive and muscle invasive are two common clinical phenotypes of bladder cancer ^[2-5], corresponding to different symptoms, such as low-grade and high-grade ^[6]. Urine cytology, cystoscopy and radiological imaging are the primary approaches for BC diagnosis. However, approximately 25% of newly diagnosed patients of BC present with muscle invasive disease. Patients with non-muscle invasive also have a high risk of progression to muscle invasive without timely detection and treatment. Therefore, early detection and intervention have showed to be highly beneficial in BC treatment ^[6-8].

Metabolic fingerprinting is a strategy to investigate systematic changes in the metabolic process of a living organism, which is affected by diseases or external influences, such as exposure to medicine treatment and drugs ^[9-11]. Among recent metabolomics studies, the nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with multivariate statistical analysis were widely used to diagnose and monitor bladder cancer ^{[8, 12-16]}. Pratima et al., obtained global metabolic profiles of BC and benign adjacent tissues by non-destructive HR-MAS NMR and they further distinguished BC from benign tissues by their metabolite characteristics, which some target metabolites were further identified by a GC-MS approach ^[13]. Putluri et al., performed liquid chromatography-mass spectrometry (LC-MS) based global metabolic profiling of BC tissues/urine and identified several potential biomarkers ^[16]. Issaq et al., compared urine metabolic profiles of 48 healthy individuals and 41 patients with urothelial carcinoma ^[17]. HPLC-MS was used to profile the metabolic features of these two groups.

Chemometrics is necessary to pre-treat and analyze these complex data and to ensure the reliability of these metabolic fingerprints ^{[18, 19]}, such as baseline correlation, background noise and retention time shifts. After pre-treatment, the processed metabolite fingerprints were analyzed by various unsupervised (PCA, Cluster analysis) and supervised statistical methods, to create robust mathematical models for distinguishing metabolic differences between disease and healthy groups ^[20-23]. Furthermore, some key metabolites were identified as biomarkers candidates.

In the current study, a metabolomics strategy was proposed by combining the GC-MS analytical technique with some chemometrics analysis algorithms to explore the metabolic differences among different stages of BC mouse models (control group, low-grade group, middle-grade group and high-grade group). The urine samples from different bladder cancer stage mouse groups were detected by GC-MS to obtain the metabolic profiling. Subsequently, some fingerprints processing techniques (baseline correlation, background noise and retention time shifts) were performed to eliminate the non-informative signals and to reduce variations among different samples. Finally, the processed metabolites profiles were analyzed by random forest algorithm to discriminate the differences among different cancer stages and some key metabolites were discovered as potential biomarkers by indicating different clinical characteristics of different cancer stages.

2.   Materials and Methods

2.1   Reagents

High-performance liquid chromatography (HPLC) grade methanol (CH₃OH) was purchased from Jiangsu Han Bang Tech. Co. Ltd. (China). Internal standard heptadecanoic acid (C17:0) and Urease (analytical grade) were purchased from the Sigma-Aldrich Company (St. Louis, MO, USA) and derivatization reagents methoxamine, pyridine and Bis(trimethylsilyl)trifluoroacetamide (BSTFA) were all analytical grade and obtained from the Sigma-Aldrich Company (St. Louis, MO, USA).

2.2   Animal model

Male SD rats (n = 160) were purchased from Hunan Experimental Animal Co. LTD. (Hunan, China) and randomly classified into four groups, i.e., control group, low-grade group (15^th week), middle-grade group (25^th week) and high-grade group (35^th week). The model mice were fed water with the addition of 0.05% BBN (N-butyl-N-(4-hydroxybutyl) nitrosamine) (Tokyo, Japan) to induce bladder tumors at 15, 25 and 35 weeks. After induction by BBN for five weeks, three rats in each group were sacrificed for histological examinations interval every ten weeks and the prepared pathological sections were represented by optical microscopy using ordinary white light. These histological examinations were used to validate that the animal model was successfully established.

2.3   Preparations of samples

Using metabolic cages to collect urine samples from the bladder cancer and healthy control mice, samples were obtained each morning. Subsequently, all urine samples were centrifuged for 20 min (4000 r/m) to remove food and proteins and 150 μL supernatant was collected in a 1 mL centrifuge tube. Following, 100 μL urease (2 mg/mL) was added to the supernatant to decompose urea in the samples. After the reaction, 800 μL methanol was added, vortexed for 1 min and exposed to ultrasound in an ice-bath for 10 min. The samples were centrifuged at 12000 r/min for 10 min to obtain 500 μL supernatant, followed by drying the supernatant with N₂. The dried supernatant was then derivatized by adding 75 μL Methoxylamine/pyridine, mixing for 15 s and incubating for 1 h (70℃), followed by addition of 75 μL BSTFA + 1% TMCS for 1 h. Finally, 150 μL heptadecanoic acid was added and all samples were analyzed by GC-MS in random order.

2.4   GC-MS analysis

The GC-MS analysis conditions were listed as follow: all samples were analyzed on a 7890A-5975 gas chromatograph-mass spectrometer (Agilent Technology, USA), equipped with a fused-silica capillary column DB-5 MS (30 m × 0.25 mm × 0.25 μm). The temperature program of the oven was set as: 70℃ for 5 min and then ramped to 160℃ at 20℃ /min, 4℃ /min to 180℃, 10℃ /min to 300℃ and held for 1.5 min. The injected sample was 1.0 μL and the split ratio was 10:1. The temperatures of the injector, ionization source and transfer were 280℃, 200℃ and 250℃, respectively. Mass range 50 to 800 m/z, detect voltage 0.9 kV and a 3.5 min solvent delay were set.

2.5   Chemometric Method

Mass spectrometry fingerprinting has being widely used for profiling of biological samples. Data-processing technologies play important roles in managing metabolic profiles for utilization in chromatographic profiles ^{[10, 24]}. Some data processes are needed to ensure the quality and reliability of the metabolites information. In this study, the baseline removal and retention time alignment were implemented.

2.5.1   Baseline removal

For the baseline removal, adaptive iteratively reweighted Penalized Least Squares (airPLS) algorithm was used to remove the baseline for all the chromatography profiles. The airPLS was proven to be a mature and effective algorithm ^{[25, 26]}. The whole airPLS process is iteratively changing weights of sum squares errors (SSE) obtained using the differences between the previously fitted baseline and the original chromatogram. A fitted baseline is generally approached and ultimately obtained as a baseline removed chromatogram.

2.5.2   Retention time alignment

The combination retention time alignment approach was established based on recursive alignment by fast Fourier transform (RAFFT) and chromatogram alignment via Mass spectra (CAMS) ^{[27, 28]}. This combination approach has shown its advantages in managing chromatographic signals in previous research ^[29]. The detailed algorithms for RAFFT and CAMS can be found in ^{[27, 28]}. Here, we briefly describe both algorithms as follows:

RAFFT makes use of a recursive segmentation model and it is necessary to estimate a minimal segmentation size before performing alignment. Subsequently, the CAMS method is used to further manage the signal. CAMS method makes full use of the hyphenated chromatographic information from both chromatographic and spectral directions. The CAMS algorithm contains the following steps: peak detection, candidate shift detection via fast Fourier transform cross correlation, candidate shifts validation via mass spectra and move peaks. In order to speed up the whole alignment procedure, the fast Fourier transform is applied to calculate the cross correlation, which reduces the time complexity of cross correlation.

2.5.3   Random forest data analysis

Random forest was successfully used in various studies, such as QSAR ^{[30, 31]}, food and herbal quality control ^{[32, 33]} and metabolomics ^{[34, 35]} and has shown its advantages in managing complex and non-linear data matrix. Random forest is a tree-based assemble method ^[36]. The final classification result was obtained by assembling all the classification results obtained by each tree model. The differences between each model can affect the model accuracy; therefore, the RF method uses different samples and differences variables in each tree model to increase the "randomness", which can effectively increase the difference of the whole model and increase the accuracy of the classification model. All data processing and calculations in this work were performed using MATLAB R2010 ^a.

3.   Results

3.1   Metabolic profiles process and samples analysis

The TIC chromatography fingerprints of four classes of mouse urine samples are shown in Fig. 1. The metabolites in different groups were similar; however, the concentrations of each metabolite were different. Further, we found that the retention times of some metabolites for each group's metabolic profile fingerprints were slight different or shifted. Suitable profile processing is benefit to the metabolites identification. In current study, three profile processing methods were used to optimize the results. The whole fingerprints process contains several steps: first, the whole fingerprints lengths were set to the same length; second, all the metabolic fingerprints were detected to remove baseline and align the retention times. The processed sketch of these metabolic profiles can be seen in Fig. 2. As could be seen from Fig. 2a, the peaks without align distribution broadly; after pre-treat all peaks were almost in the same retention time (Fig. 2b). Based on these processed fingerprints, qualitative and quantitative analysis was carried out and results were listed in Table 1.

Figure  1.  The typical total ion chromatograms (TICs) of healthy control and three stages of bladder cancer mice urine samples

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Figure  2.  The align effects of metabolites profiles

a. Before align the retention times; b. After align the retention times.

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Table  1.  Qualitative and quantitative metabolic profile of four group mice

Id	tra (min)	endogenous metabolites	Healthy controls	15 weeks	25 weeks	35 weeks
1	5.72	Aminoethane	30.19±6.86	29.00±8.42	38.90±4.93	13.40±8.72
2	6.493	Ethylene glycol	23.59±2.27	26.64±5.55	21.76±8.21	20.91±6.57
3	6.74	N, N-diethylacetamide	4.50±1.85	4.19±1.39	6.36±3.93	6.20±2.40
4	7.62	Lactic acid *	9.01±1.48	9.11±1.14	9.87±3.18	4.38±0.58
5	7.845	Acetic acid	19.64±3.93	18.70±2.49	22.83±3.87	16.54±1.08
6	10.01	phosphate	141.41±26.08	128.64±37.76	128.14±60.14	119.97±7.99
7	10.33	l-Threonine	5.19±2.42	3.89±1.09	15.28±4.62	5.71±2.22
8	10.497	Phenylacetic acid	51.64±44.04	39.25±28.72	75.15±53.41	42.76±34.59
9	10.582	Succinic acid *	7.76±5.49	7.06±2.83	11.04±4.90	6.53±0.30
10	10.747	1, 2-Hydroquinone	17.38±3.13	17.99±2.69	14.08±6.29	6.97±1.16
11	10.903	Glyceric acid	1.12±1.18	0.49±0.66	0.37±0.24	0.28±0.17
12	10.94	(R*, R*)-2, 3-Dihydroxybutanoic acid	163.76±34.02	143.76±18.98	179.86±31.52	208.52±67.82
13	11.757	2, 4-Dihyoxybutanoic acid	7.49±5.42	7.16±5.52	7.26±3.16	5.41±2.86
14	11.983	(R*, S*)-3, 4-Dihydroxybutanoic acid	1.15±0.37	1.22±0.19	1.74±0.48	0.80±0.48
15	11.797	N-(1-oxobutyl)- Glycine	6.40±1.77	6.19±3.89	4.28±1.59	3.39±1.41
16	12.202	Isovaleroglycine	0.46±0.21	0.46±0.22	0.74±0.43	0.78±0.35
17	12.412	D- Threitol	1.53±0.89	1.63±0.97	1.74±1.69	2.85±3.38
18	12.645	N-Crotonyl glycine	4.39±1.11	4.77±0.57	7.52±2.13	6.09±1.82
19	12.973, 13.203	2, 3, 4-Trihydroxybutyrate	3.25±1.07	2.68±0.63	3.49±1.49	6.51±1.08
20	13.416	N-(1-oxohexyl)-glycine	7.53±3.03	7.22±4.44	3.88±1.66	5.84±5.84
21	14.58	3-Hydroxyphenylacetic acid	0.97±0.23	1.08±0.55	1.38±0.80	7.73±4.79
22	14.713	D-Xylose	1.81±1.69	2.15±0.71	1.96±1.49	2.72±1.00
23	14.823, 15.057	D-Ribose	7.51±2.46	1.49±1.49	1.56±2.53	0.11±0.32
24	15.509, 15.733	Arabitol	4.96±1.26	3.74±0.49	3.15±0.79	2.77±1.22
25	16.023	6-Deoxy-D-Galactose,	1.43±0.50	2.31±1.02	2.47±2.23	5.57±2.59
26	16.087	Mannonic acid	17.97±5.52	11.06±2.48	9.92±2.37	7.49±8.92
27	16.42	cis-Aconitic acid*	7.13±9.11	5.43±10.64	0±0	11.42±10.58
28	16.35	Phosphoric acid	5.31±1.42	4.31±3.67	3.72±1.39	3.23±2.70
29	17.277	Isocitric acid*	145.25±21.71	168.62±181.3	138.57±22.79	124.34±23.93
30	17.563	Hippuric acid	1.29±0.67	0.79±0.63	0.86±0.64	2.02±0.46
31	17.85,	D-Fructose*	3.47±3.11	4.71±3.18	3.73±7.23	8.73±2.92
32	18.018	N-phenyl glycine*	1.63±0.40	1.56±0.81	1.51±0.72	1.01±0.83
33	18.297,	D-Glucose*	3.73±2.17	2.55±0.88	2.39±1.52	0.91±0.98
34	18.566	Altronic acid	75.57±24.74	66.84±12.99	67.08±7.42	69.33±22.33
35	18.65	D-Sorbitol*	4.69±6.51	7.87±7.67	8.98±11.88	4.30±5.64
36	18.983,	Galactonic acid	1.79±0.76	1.48±0.62	1.09±0.79	0.32±0.09
37	19.89	Palmitic acid	20.19±4.33	19.00±4.78	28.90±4.93	15.40±6.72
38	20.42	Myo-Inositol	12.59±2.27	16.64±2.55	11.76±3.21	10.91±2.57
39	25.45	D-Turanose	14.50±1.85	12.19±1.39	9.36±1.93	8.25±2.12
40	25.65	D-(+)-lactose monohydrate*	6.01±0.78	7.11±1.04	8.87±2.18	6.38±0.58
41	25.927	Lactose*	9.64±3.36	8.70±2.59	12.83±3.28	9.57±1.78

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3.2   Pattern recognition and biomarker creening

In this section, we aimed to use the random forest (RF) algorithm to classify the four different groups and we found some potential biomarkers by indicating the differences of each group. The advantages of the random forest algorithm are that it can manage multi-classes classification and the variable importance calculation (biomarkers identification). The classification plots of four groups are shown in Fig. 3. Four groups of mice were located into four different areas. These results suggested that bladder cancer caused metabolic changes. Furthermore, the metabolites changed with the disease progress, such that the early stage and middle stage mouse samples were different from the advanced stage. These results provided us some evidence to monitor the metabolites variations and further discriminate the differences among the four groups.

Figure  3.  The MDS plot for urine metabolic fingerprints for four groups mice

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During the model establishment process, RF can calculate the importance for each variable in distinguishing different stages in mice. The importance features of each variable are shown in the Fig. 4. Four metabolites, glyceric acid, (R^*, R^*)-2, 3-Dihydroxybutanoic acid, N-(1-oxohexyl)-glycine and D-Turanose, showed large contributions to discriminate the four mouse groups. These metabolites can be used as potential biomarkers for further analysis and can be used to aid early diagnosis of bladder cancer.

Figure  4.  The variable importance measures of four groups mice obtained by RF

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4.   Discussion

Fingerprints processing algorithms can help improve the precision of metabolic profiles. Three algorithms, airPLS, RAFFT and CAMS, were used for fingerprints baseline removal and retention time alignment in our previous studies and they showed their advantages in improving the precision of metabolites profiles ^{[27, 28]}. Therefore, in this study, we combined these three algorithms together to analyze these metabolites profiles.

During the model establishment process, two parameters in RF were set up: the number of "tree" in the model and the number of variables in each tree model. The optimization of suitable parameters for classification model is necessary. As can be seen from Fig. 5, when the tree number is 6000, the classification error reached the lowest. Therefore, in current study, the tree number was set as 6000, while the variable number used in each model was set at 7 as a default parameter. Based on these setting, a classification model could be established to discriminate the differences among the bladder cancer metabolic fingerprints.

Figure  5.  Selection of tree number for RF

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5.   Conclusion

In this study, a metabolomics strategy was proposed to discriminate four groups of mice with bladder cancer. Metabolites fingerprints were used to describe the characteristics of each group. The chemometrics-aided metabolites fingerprints can aid better understanding of the molecular changes of bladder cancer progression. Some potential biomarkers were identified, which can be used to aid early diagnosis of BC.

Acknowledgements

We thank for the funding support from the Natural Science Foundation of China (No. 81673585 and No. 81603400); Hunan Provincial Key Laboratory of Diagnostics in Chinese Medicine Open Fund (No. 2015ZYZD13 and No. 2015ZYZD10); Key research and development project of Hunan Province Science and Technology (No. 2016SK2048); Innovative Project for Post-graduate of Hunan University of Chinese Medicine (No. 2017CX05); and the National Standard Project of Chinese Medicine (No. ZYBZH-Y-HUN-21).

Competing Interests

The authors declare no conflict of interest.