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. 2022 Aug 16;17(8):e0266730. doi: 10.1371/journal.pone.0266730

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1H NMR

Jie Zeng 1, Jin Peng 2,*, Hua Jiang 1, Pengchi Deng 3, Kexun Li 1, Daolin Long 1, Kai Wang 1
Editor: Oscar Millet4
PMCID: PMC9380946  PMID: 35972924

Abstract

Objective

To prospectively establish an early diagnosis model of acute colon cancerous bowel obstruction by applying nuclear magnetic resonance hydrogen spectroscopy(1H NMR) technology based metabolomics methods, combined with machine learning.

Methods

In this study, serum samples of 71 patients with acute bowel obstruction requiring emergency surgery who were admitted to the Emergency Department of Sichuan Provincial People’s Hospital from December 2018 to November 2020 were collected within 2 hours after admission, and NMR spectroscopy data was taken after pretreatment. After postoperative pathological confirmation, they were divided into colon cancerous bowel obstruction (CBO) group and adhesive bowel obstruction (ABO) control group. Used MestReNova software to extract the two sets of spectra bins, and used the MetaboAnalyst5.0 website to perform partial least square discrimination (PLS-DA), combining the human metabolome database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to find possible different Metabolites and related metabolic pathways.

Results

22 patients were classified as CBO group and 30 were classified as ABO control group. Compared with ABO group, the level of Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicyluric acid, Ferulic acid, Kynurenic acid, CDP, Mandelic acid, NADPH, FAD, Phenylpyruvate, Allyl isothiocyanate, and Vanillylmandelic acid increased in the CBO group; while the lecel of L-Tryptophan and Bilirubin decreased. There were significant differences between two groups in the tryptophan metabolism, tyrosine metabolism, glutathione metabolism, phenylalanine metabolism and synthesis pathways of phenylalanine, tyrosine and tryptophan (all P<0.05). Tryptophan metabolism pathway had the greatest impact (Impact = 0.19). The early diagnosis model of colon cancerous bowel was established based on the levels of six metabolites: Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicylic acid, Ferulic acid and Kynurenic acid (R2 = 0.995, Q2 = 0.931, RMSE = 0.239, AUC = 0.962).

Conclusion

This study firstly used serum to determine the difference in metabolome between patients with colon cancerous bowel obstruction and those with adhesive bowel obstruction. The study found that the metabolic information carried by the serum was sufficient to discriminate the two groups of patients and provided the theoretical supporting for the future using of the more convenient sample for the differential diagnosis of patients with colon cancerous bowel obstruction. Quantitative experiments on a large number of samples were still needed in the future.

Introduction

Due to changes in dietary patterns, obesity, and the prevalence of smoking, the incidence of colorectal cancer in East Asia and other areas where the incidence of colorectal cancer was low in the past is increasing rapidly [1]. At the same time, due to traditional reasons such as awareness of medical care, a large number of colorectal cancer patients in China are already in the advanced stages when they are diagnosed. Colon cancerous bowel obstruction (CBO) is a frequent end-stage event in patients with advanced colon cancer, and its treatment effect is also extremely pessimistic. This is prominently reflected in the contradiction between the uncertainty of preoperative diagnosis and the urgency of surgical intervention, the wide surgical indications and the high difficulty of operation, as well as the poor prognosis of treatment and the high expectations of patients [2]. Although NCCLS established the diagnostic criteria for Malignant bowel obstruction (MBO) in 2007 [3], it has been found that its application is limited by the dependence on imaging evidence and the low specificity of tumor-associated antigens in clinical practice. Patients with CBO have experienced a systemic metabolic disorder that is regulated by hormones, proteins, and small molecule chemical groups produced from metabolism. If we could understand and depict the complete metabolome of patients with CBO, It’s possible to quickly assess the patient’s condition and predict its prognosis. In the past ten years, the research on the metabolic profile of patients with colorectal cancer has mostly focused on the early diagnosis and staging of tumors. It was rare to describe the metabolic characteristics of colon cancerous patients with bowel obstruction. This research is based on 1H- NMR, using chemometrics and mathematical methods to construct a diagnostic model for CBO patients for the first time. It’s hoped that the clinical goals of accurate diagnosis and precise treatment could be achieved through the establishment of the metabolic profile of CBO.

Materials and methods

Research object

The emergency patients who attended the Emergency Surgery Department of Sichuan Provincial People’s Hospital from December 2018 to November 2020 were enrolled. The research was approved by the ethics review committee of Sichuan Provincial People’s Hospital (NO. 2018[233]), and all patients signed the written informed consent before participating in the study.

1. Inclusion criteria: (1) Older than 18 years; (2) Hospital admission for acute abdomen; (3) Positive clinical evidences of bowel obstruction [including medical history, physical examination and imaging evidences]; (4) Negative digital anal examination; (5) Fasting for more than 24 hours; (6) Positive emergency exploratory Indications (whole abdomen Tenderness and rebound pain; bowelsound <1 per min).

2 Exclusion criteria (1) Combined with hyperthyroidism, diabetes and other metabolic diseases, or accompanied by neurological diseases that affect metabolism; (2) For patients with abnormal liver and kidney function; (3) Long-term administration of hypoglycemia/antihyperlipidemic agents, thyroxine tablets or other drugs that affect metabolism; (4) Sepsis; (5) Pregnant women; (6) Patient got medical procedures (e.g., oral or intravenous administration, gastrointestinal decompression, enemas) within 48 hours; (7) Patient got imaging examination using contrast agents within 48 hours; (8) Patient got intravenous or oral rehydration prior to specimen collection.

3. Sample size According to the inclusion and exclusion criteria above, the study included 71 patients with acute bowel obstruction. Then 7 cases of severe metabolic abnormalities were discharged (5 cases of metabolic disease that were diagnosed for the first time, 2 cases of severe abdominal infection); 7 cases of other neoplastic bowel obstruction (3 cases of intestinal lymphoma, 4 cases of extraintestinal malignant lesions invasion) were discharged; 5 cases were discharged without postoperative pathological diagnosis (2 cases refused surgery, 3 cases could not obtain pathological results). 22 patients were included in the colon cancerous bowel obstruction group (CBO), while 30 patients were in the adhesive bowel obstruction group (ABO).

Experimental method

1 Patient information and specimen collection

1.1 General information and collection of pathological results. The postoperative paraffin pathological results were checked 1 week after the operation and the patients who met the enrollment criteria were registered. The general information included name, gender, age, Body Mass Index(BMI), carcinoembryonic antigen(CEA) and preoperative abdominal CT.

1.2 Specimen collection. Collected 3ml whole blood into vacuum blood collection tubes (Blue cap with sodium citrate, 10.25mm×64mm, batch number 363095, American BD company) from the peripheral veins of all participants within 2 hours after admission, These samples were all processed within 30 minutes. The tube was centrifuged at 16000r/min for 10 minutes, then the supernatant was taken and transferred to EP tube, which was refrigerated at -80°C finally.

2 Sample processing

Placed—80°C sample to room temperature firstly. 60μl D2O was added into an EP tube with 450μl sample, and vortexed for 30 seconeds in a vortex machine. Then the tube was transferred to a 5mm Wilmad NMR tube for computer testing and analysis lastly.

3 Sample testing

A Bruker 600MHz NMR spectrometer (DRX 600MHz NMR, Bruker Biospin Rheinstetten, Germany) was used to detect the NMR tube, and all one-dimensional 1H-NMR spectra were obtained. The proton frequency was set to 600.1 MHz and a 300K cryogenic probe was used. The 1H-NMR experiment used the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to obtain one-dimensional NMR spectra from each sample. The spectrum was collected when the spectral width was 20 ppm and the relaxation time was 5 seconds.

4 Data export

Used the mestRenova software (version 12.0.0, MestreLab Research, Spain) to open all the spectra that need to be processed and generated a new overlapped spectrum. The Fourier transform is completed after manual phasing. After normalization, the integration range was set to 0.04-8ppm, the integration interval was 0.04ppm, the spectrum area was divided into 199 intervals of equal width, and the spectral bins were obtained.

5 Data analysis

Firstly, used MetaboAnalyst 5.0 to perform partial least squares discrimination (PLS-DA) on the spectral bins, and obtained interval ranking and principal components analysis model with Variable important in projection (VIP)>1, then perform cross validation. Secondly, confirmed the different metabolites by using the Human Metabolome Database (HMDB), combining published research articles and comparing with standard data. Then, analyzed the the peak intensities to evaluate the reliability of the different metabolites between the two groups. Combined with Kyoto Encyclopedia of Genes and Genomes (KEGG), MetaboAnalyst 5.0 was used to analyze different metabolites and their potential metabolic pathways lastly.

6 Statistical methods

Chi-square test (Fisher’s exact probability test) and t-test (Student’s t-test or Wilcox test) were used to analyze categorical variables and continuous variables, and p <0.05 was considered to have a significant difference. Partial least squares discriminant analysis (PLS-DA) was used for multivariate analysis of metabolic differences between the two groups. Debiased Sparse Partial Correlation (DSPC) was used to perform pairwise correlation analysis of differential metabolites. The AUC of the ROC curve was calculated to judge sensitivity.

Results and discussion

Results

1. Baseline data

In this study, a total of 71 patients were enrolled, in which 19 abnormal cases were discharged, and a total of 52 valid cases were collected. Among them, 22 were confirmed CBO patients and 30 were ABO patients. General informations are in Table 1. The positive rate of CT (AUC = 0.735, CI: 0.591–0.879, P = 0.004) and the level of CEA (AUC = 0.644, CI: 0.488–0.800, P = .048) of the CBO group are significantly higher than those of the ABO group(all P<0.05).

Table 1. Baseline data.
CBO ABO P
Age (mean±sd years) 67.773±14.3425 60.933±17.1302 0.135
Height (mean±sd cm) 159.727±7.8630 162.467±8.4271 0.239
Weight (mean±sd Kg) 54.591±8.1220 55.567±10.8824 0.725
BMI(mean±sd) 21.541±2.9730 21.04±3.1169 0.562
Gender(M/F) 9/13 20/10 0.065
CT positive rate (%) 63.6(14/22) 16.7(5/30) 0.001
CEA (mean±sd ng/ml) 23.7850±16.75588 3.8727±3.59272 0.021
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2. 1H NMR metabolic spectrum of serum samples

The 1H NMR spectrum of the two groups of serum samples were overlapped with the mestRenova software after processing. It can be seen that there is no significant difference between two groups (Figs 1 and 2). Set the integration range from 0.04 to 8ppm and the integration interval in 0.04ppm. The spectrum area is divided into 199 intervals of equal width to obtain the spectral bins.

Fig 1. CBO’s spectrums.

Fig 1

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Fig 2. ABO’s spectrums.

Fig 2

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3. Differential metabolite analysis

Firstly, Used MetaboAnalyst 5.0 to perform non-supervised principal component analysis (PCA) on the spectral bins, showing that there are significant differences between the CBO group and the ABO group (Fig 3). Driven by this observation, we continued to conduct supervised statistical analysis, namely partial least squares discrimination (PLS-DA), and found that the two groups of metabolites were well dispersed and have no obvious outliers and specific aggregation trends. Obvious trend of separation between groups is observed (Figs 4 and 5). There were 35 intervals which the Variable Important in Projection (VIP)>1 (Fig 6). They were input into the human metabolome database (HMDB) searching, and 1470 possible metabolites were obtained, of which 388 endogenous metabolites could be detected in the blood. According to the Jaccard Index Match Ratio, combined with published research articles [4–15], the top 30 metabolites were selected. By comparing standard spectra with HMDB (1H NMR Spectrum [1D, 600 MHz, D2O, anticipated]), it was shown that 15 metabolites had the lowest peaks at chemical shifts that overlapped with the above 35 intervals. Mesrenona software was used to determine the peak intensities of these 15 shifts separately, and a t-test revealed that the peak intensities of six shifts were substantially different. Six chemical shifts correspond to six metabolites, and the integrals of the lowest peak intensity are notably different (Table 2 and Fig 7). Compared to the ABO group, the level of Xanthurenic acid (XA), 3-hydroxyanthranilic acid (3-HAA), Gentisic acid (GA), Salicyluric acid (SUA), Ferulic acid (FA), Kynurenic acid (KA), CDP, Mandelic acid (MA), NADPH, FAD, phenylpyruvate (PPA), Allyl isothiocyanate (AITC) and Vanillylmandelic acid (VMA) increase relatively; while the level of L-tryptophan (L-Try) and bilirubin (BILI) decrease relatively in the CBO group. Performed K-fold cross validation to prevent overfitting to obtain a discriminant model for CBO by MetaboAnalyst 5.0: Comp1 = 176(interval) *0.228+188 *0.167+ 144 *0.056+ 175 *0.049+ 183 * 0.033+ 168 *0.029 (Accuracy = 1, R2 = 0.995, Q2 = 0.931, RMSE = 0.239, K = 10, AUC = 0.962 (95%CI: 0.878–1)) (Figs 7 and 8). Divided the above coefficient by the numbers of hydrogen atoms of the metabolite corresponding to the interval to get the following model: Comp1 = XA *0.033+-3-HAA *0.024+ GA *0.009+ SUA *0.005+ FA * 0.003+ KA *0.029. This model has the largest area under the curve (AUC) (0.932(CI: 0.869–0.995)) compared with those six differential metabolites’ peak intensity through univariate receiver operating characteristic curve (ROC) analysis [16] (Fig 9).

Fig 3. PCA scores plot between the Comp1 and Comp2.

Fig 3

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Fig 4. Pairwise scores plots between the selected components.

Fig 4

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Fig 5. PLS-DA scores plot between the Comp1 and Comp2.

Fig 5

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Fig 6. Important features identified by PLS-DA.

Fig 6

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Table 2. Comparison of the lowest peak area of metabolites.
metabolite Interval Jaccard Minimum peak intensity integral (Mean (SD))
CBO ABO
Xanthurenic acid 176 0.158 907.964 (169.803) 757.325 (201.528) ↓*
Allyl isothiocyanate 101 0.148 28634.723 (20289.228) 24693.512 (16034.627) ↓
Kynurenic acid 168 0.143 360.680 (195.294) 266.103 (156.627) ↓*
Mandelic acid 123 0.105 814.341 (1205.804) 734.308 (1664.378) ↓
Gentisic acid 144 0.103 4296.669 (2121.122) 1752.036 (1413.397) ↓*
FAD 166 0.103 360.680 (195.294) 266.103 (156.627) ↓
3-Hydroxyanthranilic acid 188 0.098 3502.353 (13572.471) 68.253 (61.656) ↓*
Vanillylmandelic acid 172 0.098 895.535 (3099.663) 210.336 (94.918) ↓
Phenylpyruvic acid 160 0.095 -4.067 (52.152) -7.924 (33.538) ↓
NADPH 101 0.092 28634.723 (20289.228) 24693.512 (16034.627) ↓
Ferulic acid 183 0.091 601.120 (219.776) 269.936 (194.209) ↓*
Salicyluric acid 175 0.091 750.304 (103.508) 544.265 (154.842) ↓*
Bilirubin 157 0.089 -35.597 (40.246) -17.707 (41.004) ↑
L-Tryptophan 196 0.089 25.432 (98.214) 784.480 (3647.465) ↑
CDP 108 0.089 1126.090 (630.975) 956.864 (483.452) ↓
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↓: The peak intensity is lower in ABO group.

↑: The peak intensity is high in ABO group.

*: P<0.005

Fig 7. The peaks of six principal components on the spectrum.

Fig 7

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Fig 8. PLS-DA classification.

Fig 8

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The red star indicates the best classifier. (Accuracy = 1, R2 = 0.995, Q2 = 0.931.

Fig 9. Sensitivity comparison.

Fig 9

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SA: Salicyluric acid; KA: Kynurenic acid; XA: Xanthurenic acid; HAA: 3-Hydroxyanthranilic acid; GA: Gentisic acid; FA: Ferulic acid; WHOLE: The prediction model, Comp1 = XA *0.033+3-HAA *0.024+ GA *0.009+ SUA *0.005+ FA * 0.003+ KA *0.004.

4 Metabolic pathway analysis

Though analyzing the above 15 differential metabolites’ pathways by using pathway enrichment analysis and topological analysis in MetaboAnalyst 5.0 combined with Kyoto Encyclopedia of Genes and Genomics (KEGG), 9 metabolic pathways that may be affected between groups were obtained. There are significant differences in tryptophan metabolism, tyrosine metabolism, glutathione metabolism, phenylalanine metabolism and phenylalanine-tyrosine-tryptophan synthesis pathways between the two groups (all P<0.05) Table 3. Moreover, the impact of the tryptophan metabolic pathway is greater than 0.1, and the number of accurate matching metabolites is 2. This metabolic pathway is considered to have the greatest impact on the metabolic pathway difference between the CBO group and the ABO control group (Fig 10). Then, the diagrams of different metabolic pathways and tryptophan metabolism and tyrosine metabolism pathways are drawn according to KEGG (Figs 11 and 12). Finally, the pairwise correlation of differential metabolites were analyzed by using Debiased Sparse Partial Correlation (DSPC), suggesting that GA and KA (P = 3.45e-07, PACF = 0.84), GA and VMA (P = 7.7 e-05, PACF = 0.501), XA and CDP (P = 4.07e-06, PACF = 0.698) levels showed significant positive correlation, while there was negative correlation between L-Try and GA (P = 0.0016, PACF = -0.34), Try and XA (P = 0.0282, PACF = -0.227), KA and CDP (P = 0.0398, PACF = -0.285) levels.

Table 3. Result from pathway analysis by metaboanalyst5.0.
Pathway Name Match Status p -log(p) Holm p FDR Pathway Name
Tryptophan metabolism 2/41 0.0047345 2.3247 0.04261 0.030557 0.19462
Tyrosine metabolism 2/42 0.00748 2.1261 0.05984 0.030557 0.00115
Glutathione metabolism 1/28 0.01289 1.8898 0.090228 0.030557 0.02698
Phenylalanine metabolism 1/10 0.016976 1.7702 0.10186 0.030557 0.2619
Phenylalanine, tyrosine and tryptophan biosynthesis 1/4 0.016976 1.7702 0.10186 0.030557 0
Pyrimidine metabolism 1/39 0.091057 1.0407 0.36423 0.13659 0.01726
Aminoacyl-tRNA biosynthesis 1/48 0.1078 0.9674 0.36423 0.13859 0
Porphyrin and chlorophyll metabolism 1/30 0.14884 0.82729 0.36423 0.16625 0.05288
Riboflavin metabolism 1/4 0.16625 0.77924 0.36423 0.16625 0
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Fig 10. The view of nine affected metabolic pathways between two groups.

Fig 10

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1 Tryptophan metabolism pathway, 2 Tyrosine metabolism pathway, 3 Glutathione metabolism pathway, 4 Phenylalanine metabolism, 5 Phenylalanine, tyrosine and tryptophan biosynthesis, 6 Pyrimidine metabolism pathway, 7 Aminoacyl-tRNA biosynthesis pathway, 8 Porphyrin and chlorophyll metabolism pathway, 9 Riboflavin metabolism pathway.

Fig 11. The view of tryptophan and tyrosine metabolism pathway.

Fig 11

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C00078 L-Tryptophan; C01717 Xanthurenate; C00632 3-Hydroxyanthranilic acid; C00247 Kynurenic acid; C00006 NADPH(Glutathione metabolism); C00628 Gentisic acid; C05584 Vanillylmandelic acid.

Fig 12. The KEGG metabolism pathway map.

Fig 12

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1, Tryptophan metabolism 2, Tyrosine metabolism 3, Glutathione metabolism 4, Phenylalanine metabolism 5, Phenylalanine, tyrosine and tryptophan biosynthesis 6, Pyrimidine metabolism chlorophyll metabolism 7, Porphyrin and chlorophyll metabolism 8, Riboflavin metabolism.

Discussion

The fast, untargeted NMR-metabolomic fingerprinting of biofluids has the ability to identify the individual metabolic phenotype and the signature of different diseases [17]. The difference in serum metabolomics between patients with colorectal cancer (CRC) who are not in a state of obstruction and healthy people is mostly reflected in the differences in glycolysis represented by pyruvate and lactic acid, and differences in glycerol and fatty acid metabolism represented by hydroxybutyric acid [4, 6, 7, 13, 15, 18, 19]. This study used 1H NMR to analyze the serum metabolites of patients with colon cancerous bowel obstruction (CBO) and patients with adhesive bowel obstruction (ABO) for the first time, and found that the metabolic differences mainly reflect in the metabolism of aromatic amino acids between the two groups. In particular, the up-regulation of tryptophan catabolism, which is based on the kynurenine pathway, may further decrease the cellular immune function and bowel smooth muscle’s neurotransmitter conduction in CBO.

The pathological processes of bowel obstruction includes increasing secretions in the digestive tract and insufficient peripheral perfusion caused by the fluid accumulation of the third space, which leads to the decline of intestinal peristalsis, intestinal epithelial injury and inflammatory reactions. Intestinal secretions accumulate in the intestinal lumen, and when the reabsorption process fails, the intestine continues to lose fluid and electrolytes, leading to a vicious cycle of "expansion-secretion-expansion" [20], which directly leads to tissue glycolysis increasing and ischemia of Intestinal mucosa. Then there is a surge in lactic acid and fatty acids in the circulation [21]. Such metabolic changes also partially appears in the serum of non-obstructed CRC patients, which should be an important reason why the serum did not show significant differences in glycolysis and glycerol fatty acid metabolism pathways between CBO and ABO in this study. It’s found that the difference in serum metabolism between the two groups is mainly reflected in the tryptophan, tyrosine and phenylalanine metabolism pathways (all P<0.05), especially the difference in the tryptophan metabolism pathway has the greatest impact (impact = 0.19). Tan et al. [18] observed differential metabolites of tryptophan, phenylalanine and tyrosine metabolism in the non-obstructed CRC serum. These metabolites are related to the co-metabolism of gut microbes and hosts. It is speculated that abnormal metabolism of intestinal flora may be an obvious metabolic feature of CRC. Continuous intestinal obstruction causes the necrosis of intestinal mucosal cells and the decline of intestinal smooth muscle function, which in turn causes a sharp decline in intestinal barrier function. The bacterial load in the intestine may exceed the host’s defense capabilities, such as the obvious proliferation of Pseudomonas aeruginosa, Escherichia coli, Staphylococcus epidermidis, Candida and Enterococcus [22]. When obstruction occurs, CRC patients’ fragile intestinal ecological balance is further destroyed, which may be the reason why the aromatic amino acid metabolism of CBO is more active than ABO.

In mammals, tryptophan (Try) catabolism is a physiological regulation to maintain immune homeostasis and immune tolerance, which avoids acute and chronic excessive inflammation and autoimmunity [23]. Colon cancer cells (such as HT-29, Caco-2 and LC-180 cells) have a complete Try catabolism mechanism, and its decomposition rate is several times higher than that of normal colonic epithelium [24]. Try is excessively consumed due to the high expression of indoleamine 2,3-dioxygenase (IDO-1/-2) In CRC patient [25]. According to the tryptophan depletion theory [26]: Try starvation may affect this the local utilization of amino acids which inhibits the proliferation of T cells and create an immunosuppressive environment that results in the body’s tolerance to potentially immunogenic tumor antigens. When bowel obstructs, the intestinal barrier function is lost, and the enteric infection eventually causes systemic inflammatory syndrome, a large number of inflammatory mediators (such as IFN-G and IL-6) stimulate IDO1 excessive expression [27] makes Try catabolism more active in CBO patients. More than 95% of L-Try degradation produces L-kynurenine and a variety of downstream metabolites through the kynurenine pathway (KP), which are collectively referred to kynurenines (Kyns), including 3-Hydroxyanthranilic acid (3-HAA), Quinolinic acid, Xanthurenic acid (XA) and Kynurenic acid (KA), etc. [28]. In this study, the serum Try level of the CBO group was found significantly lower than that of the ABO group (t = -2.65, P = 0.011), while the CBO’s levels of XA (t = 10.43, P = 3.87E-14), 3-HAA (t = 9.1236, P = 3.21E-12) and KA (t = 4.5773, P = 3.14E-05) were significantly higher than ABO’s. Malignant tumors are prone to cause malnutrition due to their strong metabolism in the process of growth. When patients are suffering from bowel obstruction, the degree of malnutrition and cachexia increases significantly. When CRC patient suffers from inflammatory reaction caused by obstruction, the higher ratio of Kyns/Try that caused by rapid consumption of Try and the excessive production of Kyns is a possible disease identification index. Kyns/Try ratio increasing may indicate tumorigenesis [29] and ischemic necrosis of muscle cells [30]. It’s found that CBO’s serum L-Try level is negatively correlated with XA level (P = 0.0282, PACF = -0.227).

XA has a competitive inhibitory effect on the transport of L-glutamate (L-Glu) [31]. By blocking the transport of L-Glu to synaptic vesicles, it ultimately reduces the release of L-Glu from synapses, thereby glutamatergic transmission is inhibit [30]. KA is an antagonist of the Glu receptor (GluR). The overexpression of KA in the nerve injury model can block the conduction of Glu [32]. Glu is the main excitatory neurotransmitter in the central nervous system [33], and it may also act as an excitatory neurotransmitter in the enteric nervous system. The excitatory transmission of intestinal smooth muscle is mainly cholinergic in nature. Glu activates N-methyl-D-aspartic acid receptors (NRs) and promotes the release of acetylcholine of neurons in the ileum and colon [34]. Therefore, the peristalsis may be affect by the Kyns’ inhibition to the Glu transport and GluR of enteric neurons. When CRC patients suffer from obstruction, the level of Kyns increases significantly, which means that peristalsis is further weakened.

KA has also been identified as an endogenous aryl hydrocarbon receptor (AhR) agonist [35]. In addition to impairing immune T cell function through tryptophan starvation, high levels of KA produced by DCs and tumors under inflammatory conditions caused by bowel obstruction can activate IL-10 in DCs and NK cells though AhR activating [36] and IL6 transcription in cancer cells and macrophages [37] in the absence of Trp, which causes immunosuppression and immune tolerance. 3-Hydroxyanthranilic acid (3-HAA) is an intermediate metabolite and precursor of the excitotoxin Quinolinic acid with high redox activity [38]. It inhibits T cells’ expression and increases the percentage of Tregs by inducing the expression of heme oxygenase 1 (HMOX-1) [39], consuming intracellular glutathione, inhibiting cytokine release [40], inducing T cells that produce antigen-specific IL-10 [41] and inhibiting PDK1 expression [42], therefor the immune response is directly damaged. Compared with ABO patients, the high level of Kyns in the serum of CBO patients means more severe immunosuppression and immune escape occuring.

Gentisic acid (GA) is usually considered to be a salicylic acid’s metabolite catalyzed by CYP450 enzyme in the liver, but endogenous GA is produced by oxidation of Homogentisic acid, a common metabolite of phenylalanine and tyrosine. Then GA produces Maleylpyruvic acid which enters the pyruvate metabolism. Vanillylmandelic acid(VMA) is also a downstream metabolite of tyrosine. In this study, it ‘s found that the levels of GA and VMA have a higher correlation (P = 7.7e-05, PACF = 0.501), which proves that the tyrosine metabolism of CBO is more active than that of ABO. GA inhibits the activity of cdK1 enzyme in vitro and has a highly inhibitory effect on the cell proliferation of three different colon cancer cell lines (HcT-116, HT-29 and Mda-MB-231) [43]. Salicyluric acid (SUA) is formed by the conjugation of salicylic acid (SA) and glycine [44], and SUA is the main metabolite of SA. Endogenous SA is formed by oxidation of benzoic acid, a downstream product of phenylalanine metabolism, mediated by NADPH. SUA has a DNA repairing function, because it has a phenolic hydroxyl structure that similar to tannic acid, which has an inhibitory effect on the genotoxicity induced by mitomycin C (MMC) [45]. Endogenous Ferulic acid (FA) is produced by caffeic acid, the downstream product of phenylalanine metabolism, under the action of methyltransferase. It’s an antioxidant that can reduce the strength of various inflammatory mediators. At the same time, its phenolic core and expanded side chain conjugationt structure make it have anti-cancer properties. It interferes the signal pathways that control cell growth in cells, and activates programmed cell death and oxidative stress-related responses [46]. Resveratrol combined with Ferulic acid can inhibit 3D proliferation in vitro by increasing the expression of p15 in colon cancer HCT116 cells [47]. It’s found that the serum levels of GA (t = 6.8674, P = 9.68E-09), SUA (t = 6.2593, P = 8.64E-08) and FA (t = 4.9326, P = 9.38E-06) of CBO patients are significantly higher than those of ABO patients. These three metabolites that belong to phenylalanine and tyrosine metabolism pathways can exert anti-cancer effects through different mechanisms in different cell types. Therefore, whether up-regulating the phenylalanine and tyrosine metabolism pathways can inhibit the growth of colon cancer cells requires further research.

Conclusions

This study confirms that the serum metabolic differences are mainly reflected in the tryptophan catabolism between the CBO and ABO by 1H-NMR analysis. Serum metabolomics can distinguish CBO from ABO, and the entire NMR spectrum can be used as a collective "biomarker", which convinces us that there is a feasibility to develop a new serum metabolites tool for CBO diagnosis. The nuclear overhauser effect spectroscopy (NOESY) assay was not performed in this research, so there should be flaws in the identification of lipoproteins [48], but our original purpose is to conduct small molecule observations, and the analysis of macromolecular proteins will be further discussed in future experiments. Meanwhile, the sample size of this research is very small. In order to discover highly specific and sensitive metabolic markers that can help clinicians making rapid decision, the comprehensive application of various metabonomics techniques(GC-MS, LC-MS), the unification of sample collection standards and the verification of larger populations are necessary.

Supporting information

S1 Data

(DOCX)

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Abbreviations

1H-NMR

nuclear magnetic resonance hydrogen spectroscopy

3-HAA

3-hydroxyanthranilic acid

ABO

adhesive bowel obstruction

AhR

aryl hydrocarbon receptor

AITC

allyl isothiocyanate

BILI

bilirubin

BMI

body mass index

CBO

colon cancerous bowel obstruction

CEA

carcinoembryonic antigen

DSPC

debiased sparse partial correlation

FA

ferulic acid

GA

gentisic acid

Glu

glutamate

GluR

Glu receptor

HMDB

the human metabolome database

HMOX-1

heme oxygenase-1

KA

Kynurenic acid

KEGG

the kyoto encyclopedia of genes and genomes

KP

kynurenine pathway

Kyns

kynurenines

MA

mandelic acid

NRs

N-methyl-D-aspartic acid receptors

PCA

principal component analysis

PLS-DA

partial least squares discrimination

PPA

phenylpyruvate

SUA

salicyluric acid

Try

tryptophan

VIP

variable important in projection

VMA

vanillylmandelic acid

XA

Xanthurenic acid

Data Availability

All relevant data are within the manuscript and its Supporting information files and the raw data can be download at https://github.com/dcpengjin/metabolomics_data.

Funding Statement

The study was supported by Basic scientific research business expenses of Science & Technology Department of Sichuan Province (grant no.2018YSKY0017-9), and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Kexun Li and Daolin Long who were studying for a master’s degree got Ұ12,000 as Labor wage from this foundation, respectively. Mean while, the other authors of this manuscript got no salary from the funder.

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Decision Letter 0

Oscar Millet

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

24 Nov 2021

PONE-D-21-25026

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1HNMR

PLOS ONE

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Reviewer #1: In this manuscript 1H NMR coupled with machine learning is used to build a model for the differential diagnosis of acute colon cancerous bowel obstruction (CBO) vs. adhesive bowel obstruction (ABO) in serum. Although the entire spectrum is proposed to function as “biomarker”, then the authors concentrate the discussion on six metabolites.

It would be important to know what would be the prediction accuracy, sensitivity and specificity of a model based on these metabolites rather than on the whole binned spectrum, to evaluate the contribution of the other molecules.

The NMR analysis was limited to the acquisition of CPMG spectra, thus excluding all lipoproteins components, which would help defining metabolic alterations. It is a common practice in 1H NMR metabolomics to use at least NOESY and CPMG experiments.

The serum handling procedure are largely divergent with respect to ISO standards (ISO 23118:2021 Molecular in vitro diagnostic examinations — Specifications for pre-examination processes in metabolomics in urine, venous blood serum and plasma), which could in principle affect the outcome of the downstream metabolomics analysis. Possibly the adopted procedure does not influence the comparison between the two groups of patients enrolled in the same study, but it might affect the general applicability of the model.

It would be also important to know what has been administered to the patients in the time interval between admission and blood collection (e.g. Ringer acetate solutions or contrast agents for imaging, and not just “drugs” affect the NMR metabolic profiles, Metabolomics 2015;11(6):1769-1778.).

Formal aspects:

Missing information: the data availability statement does not describe where the data can be found

Spelling:

Throughout the text: 1H NMR

Page 10, … 5 seconds

Page 12 (Discussion), … between the two groups

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PLoS One. 2022 Aug 16;17(8):e0266730. doi: 10.1371/journal.pone.0266730.r002

Author response to Decision Letter 0

23 Dec 2021

Response to academic editor：

We really appreciate you for your carefulness and conscientiousness. Your suggestions are really valuable and helpful for revising and improving our paper. According to your suggestions, we have made the following revisions on this manuscript:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

Response：Thank you for your detailed comments. We have revised the manuscript according to the PLOSOne guideline thoroughly, which are highlighted in red in the revised manuscript.

2. Please ensure that you include a title page within your main document. We do appreciate that you have a title page document uploaded as a separate file, however, as per our author guidelines (http://journals.plos.org/plosone/s/submission-guidelines#loc-title-page) we do require this to be part of the manuscript file itself and not uploaded separately.

Could you therefore please include the title page into the beginning of your manuscript file itself, listing all authors and affiliations.

Response：Thank you very much for your advice. The title page have been added into the beginning which are highlighted in red in the revised manuscript. (page 1, lines5-12)

3. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar.

A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file).

Response：Thank you for your detailed comments. We have thoroughly copyedited the manuscript for language usage, spelling, and grammar. The amendments are highlighted in red in the revised manuscript.( page 1, lines1,30,38; page3, line42; page5, lines16,18-20; page 6, lines9,12,25-26; page 7, lines11-12; page 9, lines6-7.)

4. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Response：Thank you for your detailed comments. The correct grant numbers is 2018YSKY0017-9( Basic scientific research business expenses of Science ＆ Technology Department of Sichuan Province) , which is also corrected in the ‘Funding Information’ section.

5. Thank you for stating the following in the Acknowledgments Section of your manuscript:“The present study was supported by Basic scientific research business expenses of public welfare scientific research institutes of Sichuan Province, China (grant no. 30504010428).”

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Response：Thank you for your positive comments. We have removed funding-related text from the manuscript. The study was supported by Basic scientific research business expenses of Science ＆ Technology Department of Sichuan Province (grant no.2018YSKY0017-9), and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Kexun Li and Daolin Long who were studying for a master's degree got ￥12,000 as Labor wage from this foundation, respectively. Mean while, the other authors of this manuscript got no salary from the funder.

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Response：Thank you for your detailed comments. The authors have declared that no competing interests exist, which are highlighted in red in the revised manuscript. (page 9, lines 29-30)

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Response： The full ethics statement has been included in the ‘Methods’ section of the manuscript file, which is highlighted in red in the revised manuscript.( page 2, lines 19)

Response to reviewer #1:

We really appreciate you for your carefulness and conscientiousness. Your suggestions are really meaningful and useful for revising and improving this paper. According to your suggestions, we have made the following revisions on this manuscript:

1. In this manuscript 1H NMR coupled with machine learning is used to build a model for the differential diagnosis of acute colon cancerous bowel obstruction (CBO) vs. adhesive bowel obstruction (ABO) in serum. Although the entire spectrum is proposed to function as “biomarker”, then the authors concentrate the discussion on six metabolites.

It would be important to know what would be the prediction accuracy, sensitivity and specificity of a model based on these metabolites rather than on the whole binned spectrum, to evaluate the contribution of the other molecules.

Response：We appreciate the reviewers' feedback. Regarding the accuracy, sensitivity, and specificity of the model composed of six metabolites in comparison to the entire classification spectrum for predicting CBO, we searched the HMDB for 1470 possible metabolites after obtaining 35 segments of chemical shifts with VIP values greater than 1 via PCA and PLS-DA analysis of the spectral matrix, of which 388 possible endogenous metabolites were detected in blood. According to the Jaccard value, the top 30 metabolites were selected. By comparing standard spectra with HMDB (1H NMR Spectrum [1D, 600 MHz, D2O, anticipated]), it was shown that 15 metabolites had the lowest peaks at chemical shifts that overlapped with the above 35 segments. Mnova software was used to determine the peak intensities of these 15 shifts separately, and a t-test revealed that the peak intensities of six shifts were substantially different. Six chemical shifts correspond to six metabolites, and the integrals of the lowest peak intensity are notably different S2 Table. We have revised the article in light of the reviewers' comments. (page 5, lines 2-28)

2. The NMR analysis was limited to the acquisition of CPMG spectra, thus excluding all lipoproteins components, which would help defining metabolic alterations. It is a common practice in 1H NMR metabolomics to use at least NOESY and CPMG experiments.

Response：We appreciate your kind words. Due to financial and time constraints, we have only completed the 1H NMR test at this stage. We will use GC-MS or LC-MS for the mass spectrometry test verification of the above substances in the next stage. We have also revised the article in light of the reviewers' comments. (page 8, line 41)

3. The serum handling procedure are largely divergent with respect to ISO standards (ISO 23118:2021 Molecular in vitro diagnostic examinations — Specifications for pre-examination processes in metabolomics in urine, venous blood serum and plasma), which could in principle affect the outcome of the downstream metabolomics analysis. Possibly the adopted procedure does not influence the comparison between the two groups of patients enrolled in the same study, but it might affect the general applicability of the model.

Response： Regarding the specimen handling process, we made an error expression: Because of the particularity of emergency patients (more admitted at night), our plan was to draw whole blood from the peripheral veins of all participants within 2 hours after admission, and used vacuum blood collection tubes to collect 3ml of whole blood for each participant (vacuum tube with blue cap without addition, 10.25mm×64mm, batch number 363095, American BD company), immediately put it in a refrigerator at -20℃, and centrifuge at 3000r/min for 15min within 48h, and took the supernatant , Transferred to EP tube, -80 ℃ refrigerator for refrigeration. But the actual operation was to draw 3ml of whole blood from the peripheral vein within 2 hours after admission into the tubes (Blue cap with sodium citrate, 10.25mm×64mm, batch number 363095, US BD company), centrifuged inside at 16000r/min for 15min in 30 minutes, took the supernatant, transferred to EP tube, and refrigerate at -80℃. After the specimens were collected, they were transferred to the laboratory to thaw at room temperature and transferred to a 5mm Wilmad NMR tube for on-board testing and analysis. Although the thawing temperature is different from that specified in ISO 23118:2021 and requires extended time detection, we believe that, while the serum handling procedures are different, the differences determined by the method based on the overall molecular magnetic resonance hydrogen spectroscopy profile are similar. That is, our method, PLS-DA, PCA, relies heavily on linear transformations to identify the components with the highest variation among the different grouped samples, and so is mostly unaffected by the aforementioned criteria. We have also revised the article in light of the reviewers' comments. (page 3, lines 22-29, 32-33; )

4. It would be also important to know what has been administered to the patients in the time interval between admission and blood collection (e.g. Ringer acetate solutions or contrast agents for imaging, and not just “drugs” affect the NMR metabolic profiles

Response：Thank you very much for your advice. Within 48 hours, none of our patients had medical procedures (e.g., oral or intravenous administration, gastrointestinal decompression, enemas), with the exception of fasting and water fasting for >24 hours, which we included in our exclusion criteria. No patient received imaging examination using contrast agents, and no patient got intravenous or oral rehydration prior to specimen collection. We have already revised the article in light of the reviewers' comments. (page 2, lines 38,44; page 3, lines 1-3 )

5. Formal aspects:

Missing information: the data availability statement does not describe where the data can be found.

Response：Thank you for your constructive comments. In accordance with the principles of medical ethics, the raw data of the desensitized papers can be downloaded at https://github.com/dcpengjin/metabolomics_data.git

6. Spelling:

Throughout the text: 1H NMR

Page 10, … 5 seconds

Page 12 (Discussion), … between the two groups.

Response： We appreciate the reviewers' helpful and constructive suggestions for grammar and spelling changes. All errors mentioned above have been corrected. We have already revised the article in light of the reviewers' comments. (page 1, lines 2,15; page 3, lines 41-42; page 6, lines 9,12,26)

Attachment

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Click here for additional data file. (21.7KB, docx)

Decision Letter 1

Oscar Millet

28 Feb 2022

PONE-D-21-25026R1Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1HNMRPLOS ONE

Dear Dr. Peng,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Specifically, there are still a couple of points raised by the reviewer that require to be amended.

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Academic Editor

PLOS ONE

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Reviewer #1: (No Response)

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Reviewer #1: Yes

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Reviewer #1: I believe the previous comments 1 and 2 have been misunderstood.

1. The question was very simple: what is the difference in prediction accuracy, sensitivity and specificity if the authors use the entire binned spectrum? This procedure does not require any assignment (for details see for example https://doi.org/10.1016/j.trac.2018.10.036). I invite the authors to test this approach.

2. I was suggesting to use 1H NMR NOESY spectra to derive information on the lipoprotein components (see Bruker IVDR tool; https://doi.org/10.1021/acs.analchem.8b02412), not to perform MS analyses.

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PLoS One. 2022 Aug 16;17(8):e0266730. doi: 10.1371/journal.pone.0266730.r004

Author response to Decision Letter 1

19 Mar 2022

Response to academic editor：

Your suggestions are really valuable and helpful for revising and improving our paper. According to your suggestions, we have made the following revisions on this manuscript:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response：Thank you for your detailed comments. We have reviewed our reference list thoroughly to ensure that it is complete and correct. we did not find any records about being retracted of these papers that we cited in the manuscript. In this revision, we cited another three papers.The amendments are highlighted in red in the revised manuscript.( lines195,216,339,424-427,498-500.)

Response to reviewer #1:

Your suggestions are really meaningful and useful for revising and improving this paper. According to your suggestions, we have made the following revisions on this manuscript:

1. The question was very simple: what is the difference in prediction accuracy, sensitivity and specificity if the authors use the entire binned spectrum? This procedure does not require any assignment (for details see for example https://doi.org/10.1016/j.trac.2018.10.036). I invite the authors to test this approach.

Response： The papers you recommended are very valuable to us. We performed ROC analysis on the 6 metabolites which have significant differences between the two groups and the full-spectrum prediction model. According to the AUC value, the prediction accuracy of the full-spectrum model was higher. For the verified source code see: https://github.com/dcpengjin/metabolomics_data.git. We also have revised the article in light of the reviewers' comments. (lines 192-195)

2. I was suggesting to use 1H NMR NOESY spectra to derive information on the lipoprotein components (see Bruker IVDR tool; https://doi.org/10.1021/acs.analchem.8b02412), not to perform MS analyses.

Response：Thank you very much for your professional advice and reminders. Our original purpose was to observe small molecules through a relatively faster 1H-NMR test. Due to the economic and time constraints, this study did not carry out NOESY test, so it was flawed in identifying lipoproteins, and the analysis of macromolecular proteins will be further discussed in future experiments. We have also revised the article in light of the reviewers' comments. (line 337-341)

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Decision Letter 2

Oscar Millet

28 Mar 2022

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1HNMR

PONE-D-21-25026R2

Dear Dr. Peng,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Oscar Millet

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Oscar Millet

6 Apr 2022

PONE-D-21-25026R2

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1H NMR

Dear Dr. Peng:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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PLOS ONE Editorial Office Staff

on behalf of

Dr. Oscar Millet

Academic Editor

PLOS ONE

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    Data Availability Statement

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