Colorectal cancer is one of the leading causes of tumor-associated death, and traditional Chinese medicine (TCM) classifies colorectal cancer into various subtypes mainly according to the symptomatic pattern identification
Colorectal cancer (CRC) is globally one of the most commonly diagnosed cancers, which is the fourth leading cause of death in cancer patients [
TCM, which emphasizes bringing the patient’s body, mind, and spirit into harmony, is coming to a promising and alternative approach for the prevention and treatment of tumor patients including CRC. TCM rests squarely on
Qi deficiency (QD) and Yin deficiency (YD) are two common syndromes in CRC patients. Qi refers to the vital energy of the body in TCM. It maintains blood circulation, warms the body, and fights diseases. Qi deficiency is the most common symptom in cancer patients according to the concept of TCM. Many previous reports showed that Qi supplementation can help enhance the effects of cancer therapy and the main role of TCM in cancer therapy is to balance the Qi flow in cancer patients. Yin deficiency usually represents a status of the human body under lack of nutrition and fluid and usually manifests as emaciation, dizziness, vertigo, tinnitus, dryness of the mouth, fever, and night sweats [
As an important component of systems biology, metabolomics is the study of small biological molecules found within cells, tissues, and body fluids in response to environmental, pathogenic, and dietary changes or a genetic alteration and aims to characterize and quantify all the small compounds in complex biological samples. Metabolomics, as well as genomics and proteomics, has been used to identify candidate biomarkers closely related to pathological processes of diseases [
In this study, metabolomics profiling was performed by using GC-MS to compare the difference of serum metabolic profiles in colorectal cancer subjects with ND, QD, and YD and our results demonstrate that colorectal cancer patients with QD or YD were associated with metabolic disorders and the variations of serum metabolic profiles may serve as potential biochemical markers for diagnosis and prognosis of colorectal cancer patients displayed QD or YD patterns.
This research protocol was approved by the local medical ethics committee of Zhejiang Chinese Medical University and registered in Chinese Clinical Trial Registry (registration number:
Diagnoses of all of the patients were confirmed by pathology. Trained interviewers used a uniform questionnaire to collect the TCM diagnostic information from the participants, namely, demographic factors such as age and gender, and known risk factors for CRC (including drinking, diet habit, individual disease history, marriage, and birth history). The standard criteria used for classification of CRC ZHENG were as described previously [
Advanced colorectal cancer patients meet criterions of western medicine and TCM and the following characteristics were included in the study: (a) aged between 18 and 75 years, (b) Han Chinese ethnicity, (c) newly histopathologically diagnosed with primary CRC, (d) lack of previous malignant tumors in other organs, (e) had not had antitumor therapy before recruitment, including chemotherapy and radiotherapy, and (f) did not have severe heart failure, pulmonary insufficiency, or kidney disease.
Patients with jejunum tumor, appendix tumor, colorectal adenoma, E. stromal tumor, large intestine malignant melanoma, and large intestine leiomyosarcoma and cases without pathological diagnosis and completed data were excluded.
CRC serum samples were purified through centrifugation of blood (3000 rpm, 10 min, and 25°C). Supernatant was collected and stored at −20°C until further analysis. Prior to GC-MS analysis, 1 mL of cold methanol was added to 100
One microliter of each sample was injected into the GC (Agilent 7890A/5975C) system in the splitless mode. GC separation was conducted on a capillary column HP-5MS (
The GC-MS data was processed using the automatic mass spectral deconvolution and identification system (AMDIS, version 2.71) and the metabolomics ion-based data extraction algorithm (MET-IDEA, version 2.08). Multivariate data analysis was achieved on the normalized GC-MS datasets with software package SIMCA-P (version 13.0, Umetrics, Sweden). Principal component analysis (PCA) was carried out on the dataset to generate an overview of the sample distribution and observe possible outliers. The partial least-squares discrimination analysis (PLS-DA) was further performed with the unit-variance scaled GC-MS data as
The univariate statistical analysis was performed by SPSS 19.0 for further identification of potential biomarkers, including box figure analysis and analysis of variance (ANOVA), and
A total of 90 patients with stage III-IV CRC were subjected to perform GC-MS, 30 samples for each group. Before GC-MS analysis, the association of QD and YD subtypes with patient clinicopathological characteristics was calculated. The general clinicopathological characteristics are shown in Table
Clinicopathological characteristics of studied patients.
Characteristics | Subtypes of colorectal cancer |
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---|---|---|---|---|
ND |
QD |
YD |
||
Sex | ||||
Male | 18 | 18 | 20 | >0.05 |
Female | 12 | 12 | 10 | |
Primary site | ||||
Colon | 18 | 22 | 18 | >0.05 |
Rectum | 12 | 8 | 12 | |
Tumor stage (pTNM) | ||||
III | 14 | 16 | 13 | >0.05 |
IV | 16 | 14 | 17 | |
ALT | ||||
Normal | 28 | 25 | 28 | >0.05 |
High | 2 | 5 | 2 | |
AST | ||||
Normal | 29 | 24 | 26 | >0.05 |
High | 1 | 6 | 4 | |
TBIL | ||||
Normal | 26 | 29 | 26 | >0.05 |
High | 4 | 1 | 4 | |
DBIL | ||||
Normal | 26 | 29 | 26 | >0.05 |
High | 4 | 1 | 4 | |
Scr | ||||
Low | 3 | 6 | 8 | >0.05 |
Normal | 27 | 24 | 21 | |
High | 0 | 0 | 1 | |
BUN | ||||
Low | 2 | 2 | 1 | >0.05 |
Normal | 27 | 28 | 28 | |
High | 1 | 0 | 1 | |
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Principal component analysis (PCA) was used to determine the presence of inherent similarities in spectral profiles and the corresponding PLS-DA analysis was used to identify discriminating metabolites and differentiate the two groups. PCA and PLS-DA applied to the differentially expressed metabolites (
PCA and PLS-DA scores plots discriminating CRC serum samples with QD or YD from those with ND. (a) PCA: QD versus ND; (b) PCA: YD versus ND; (c) PCA: QD versus YD. (d) PLS-DA: QD versus ND; (e) PLS-DA: YD versus ND; (f) PLS-DA: QD versus YD.
For QD versus ND, a total of 27 discriminating metabolites (VIP > 1.0,
Differential metabolites in the plasmas of CRC patients with QD compared with those with ND.
VIP |
|
rt | Name |
|
log2(QD/ND) | |
---|---|---|---|---|---|---|
Up-regulated | 1.176 | 451 | 14.43 | Diphosphate | 0.000 | 4.861 |
1.432 | 248 | 9.97 | Glycine | 0.000 | 2.431 | |
1.675 | 80 | 20.93 | Arachidonic acid | 0.000 | 2.236 | |
1.732 | 79 | 9.27 | Urea | 0.000 | 2.193 | |
1.361 | 157 | 12.70 | Pyroglutamic acid | 0.000 | 2.101 | |
1.031 | 137 | 19.63 | Oleic acid | 0.000 | 2.031 | |
1.37 | 258 | 7.43 | 3-Oxaoct-4-en-11-imine | 0.000 | 2.002 | |
1.341 | 248 | 12.07 | Aminomalonic acid | 0.000 | 1.939 | |
1.099 | 432 | 18.81 | Inositol | 0.000 | 1.907 | |
1.18 | 299 | 9.77 | L-Isoleucine | 0.000 | 1.898 | |
1.324 | 188 | 10.66 | Serine | 0.000 | 1.783 | |
1.557 | 370 | 22.69 | Monopalmitoylglycerol | 0.000 | 1.711 | |
1.638 | 181 | 9.64 | Phosphoric acid | 0.000 | 1.659 | |
1.102 | 77 | 6.22 | Carbamate | 0.000 | 1.585 | |
1.131 | 170 | 6.52 | Hydroxycyclohexane | 0.000 | 1.559 | |
1.172 | 84 | 16.32 | N- |
0.000 | 1.446 | |
1.414 | 133 | 7.73 | Oxalic acid | 0.000 | 1.415 | |
1.567 | 155 | 11.92 | GABA | 0.000 | 1.359 | |
1.526 | 57 | 27.67 | Cholesterol | 0.000 | 1.154 | |
1.543 | 155 | 11.58 | Ethylamine | 0.000 | 0.777 | |
1.168 | 217 | 17.22 | Allose | 0.000 | 0.737 | |
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Down-regulated | 1.265 | 73 | 17.06 | d-Glucose | 0.000 | −0.414 |
1.621 | 75 | 17.06 | Threose | 0.000 | −1.296 | |
1.38 | 321 | 16.98 | d-Galactose | 0.000 | −1.813 | |
1.652 | 221 | 17.04 | Mannose | 0.000 | −1.815 | |
1.191 | 205 | 16.89 | Glucopyranose | 0.000 | −2.039 | |
1.34 | 206 | 17.70 |
|
0.000 | −2.316 |
Differential metabolites in the plasmas of CRC patients with YD compared with those with ND.
VIP |
|
rt | Name |
|
log2(YD/ND) | |
---|---|---|---|---|---|---|
Upregulated | 2.176 | 451 | 14.43 | Diphosphate | 0.000 | 2.926 |
1.7 | 299 | 9.77 | L-Isoleucine | 0.000 | 1.946 | |
1.635 | 248 | 9.97 | Glycine | 0.000 | 1.818 | |
1.29 | 75 | 6.63 | Lactic acid | 0.007 | 1.319 | |
1.619 | 80 | 20.93 | Arachidonic acid | 0.001 | 1.292 | |
1.716 | 188 | 10.66 | Serine | 0.000 | 1.141 | |
1.612 | 370 | 22.69 | 1-Monopalmitoylglycerol | 0.001 | 1.046 | |
1.563 | 60 | 7.73 | Oxalic acid | 0.001 | 1.033 | |
1.567 | 248 | 12.07 | Aminomalonic acid | 0.001 | 0.973 | |
1.295 | 84 | 16.32 | N- |
0.006 | 0.877 | |
1.24 | 137 | 19.63 | Oleic acid | 0.009 | 0.818 | |
1.527 | 79 | 9.27 | Urea | 0.001 | 0.799 | |
1.061 | 192 | 13.92 | Phenylalanine | 0.027 | 0.679 | |
1.112 | 342 | 19.83 | Stearic acid | 0.020 | 0.674 | |
1.945 | 258 | 7.43 | 3-Oxaoct-4-en-11-imine | 0.000 | 0.568 | |
1.029 | 179 | 16.66 | Tyrosine | 0.032 | 0.533 | |
1.576 | 155 | 11.92 | GABA | 0.001 | 0.510 | |
1.119 | 132 | 9.22 | 5-Hydroxycaproic acid | 0.020 | 0.497 | |
1.018 | 97 | 18.08 | Palmitic acid | 0.034 | 0.462 | |
1.261 | 181 | 9.64 | Phosphoric acid | 0.008 | 0.380 | |
1.16 | 79 | 19.59 | Linoleic acid | 0.015 | 0.368 | |
1.23 | 57 | 27.67 | Cholesterol | 0.010 | 0.329 | |
1.164 | 155 | 11.58 | Ethylamine | 0.015 | 0.317 | |
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Downregulated | 1.095 | 73 | 17.06 | d-Glucose | 0.022 | −0.159 |
1.079 | 75 | 17.06 | Threose | 0.025 | −0.279 | |
1.161 | 321 | 16.98 | d-Galactose | 0.015 | −0.517 | |
1.41 | 221 | 17.04 | Mannose | 0.003 | −0.548 | |
1.19 | 205 | 16.89 | Glucopyranose | 0.013 | −0.780 | |
1.754 | 206 | 17.70 |
|
0.000 | −1.190 |
Differential metabolites in the plasmas of CRC patients with QD compared with those with YD.
VIP |
|
rt | Name |
|
log2(QD/YD) | |
---|---|---|---|---|---|---|
Upregulated | 1.181 | 451 | 14.43 | Diphosphate | 0.001 | 1.935 |
1.089 | 299 | 15.47 | Phosphoric acid propyl ester | 0.001 | 1.728 | |
1.522 | 258 | 7.43 | 3-Oxaoct-4-en-11-imine | 0.000 | 1.434 | |
1.714 | 79 | 9.27 | Urea | 0.000 | 1.395 | |
1.878 | 181 | 9.64 | Phosphoric acid | 0.000 | 1.279 | |
1.01 | 137 | 19.63 | Oleic acid | 0.003 | 1.213 | |
1.2 | 170 | 6.52 | Hydroxycyclohexane | 0.000 | 1.113 | |
1.107 | 157 | 12.70 | Pyroglutamic acid | 0.001 | 1.045 | |
1.036 | 77 | 6.22 | carbamate | 0.003 | 0.999 | |
1.202 | 273 | 16.06 | Citric acid | 0.000 | 0.992 | |
1.17 | 248 | 12.07 | Aminomalonic acid | 0.001 | 0.966 | |
1.466 | 80 | 20.93 | Arachidonic acid | 0.000 | 0.944 | |
1.028 | 79 | 19.59 | Linoleic acid | 0.003 | 0.904 | |
1.098 | 133 | 7.73 | Oxalic acid | 0.001 | 0.888 | |
1.598 | 155 | 11.92 | GABA | 0.000 | 0.849 | |
1.665 | 57 | 27.67 | Cholesterol | 0.000 | 0.825 | |
1.274 | 370 | 22.69 | 1-Monopalmitoylglycerol | 0.000 | 0.665 | |
1.203 | 341 | 19.82 | Stearic acid | 0.000 | 0.626 | |
1.261 | 155 | 11.58 | Ethylamine | 0.000 | 0.461 | |
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Downregulated | 1.04 | 73 | 17.06 | d-Glucose | 0.002 | −0.255 |
1.759 | 75 | 17.06 | Threose | 0.000 | −1.017 | |
1.561 | 206 | 17.70 |
|
0.000 | −1.126 | |
1.335 | 205 | 16.89 | Glucopyranose | 0.000 | −1.259 | |
1.622 | 221 | 17.04 | Mannose | 0.000 | −1.267 | |
1.924 | 321 | 16.98 | d-Galactose | 0.000 | −1.296 | |
1.668 | 132 | 9.22 | 5-Hydroxycaproic acid | 0.000 | −1.482 |
Metabolite set enrichment analysis (MSEA) to capture the metabolomic diversity of serum samples of CRC patients with QD or YD compared with those with ND. (a) QD versus ND; (b) YD versus ND; (c) QD versus YD.
Different metabolic pathways were strongest affected by QD or YD in CRC serum samples. (a) Venn diagram showing overlap among metabolites differentially expressed in serum samples of CRC patients with QD, YD, or ND. (b, c, and d) Schematic representation of galactose metabolism (b), tRNA synthesis (c), and alpha-linolenic acid metabolism (d) pathway. In red, the metabolites that differentially expressed in serum samples of CRC patients with QD, YD, or ND.
Heatmap is shown from hierarchical clustering analyses of metabolomics changes in serum samples of CRC patients with QD, YD, or ND.
Traditional Chinese medicine (TCM) has been widely used to relieve the symptom of colorectal cancer. Chinese medicine syndrome (CMS) is an understanding of the regularity of disease occurrence and development and correct classification of CMS groups is very important as all diagnostic and therapeutic methods in TCM are based on TCM syndrome groups. However, it is difficult to decipher the scientific basis and systematic features of CMS as of the complexity of CMS and the limitation of the present investigation method. Metabolomics enables mapping of early biochemical changes in disease and hence provides a useful tool to develop predictive biomarkers. Moreover, its method itself resembles traditional Chinese medicine (TCM) that focuses on human disease via the integrity of close relationship between the human body, fluids, and syndromes. Systemically, metabolomics has a convergence with TCM syndrome and therefore provides useful methods for exploring the essence of CMS, facilitating personalized treatment with TCM. Importantly, the integration of metabolomics and CMS will bridge the gap between Chinese and Western medicine. In the present study, we employed GC-MS to compare metabolomic profiles in serum samples of CRC patients with QD, YD, and ND. Distinctly different metabolic patterns were observed among the 3 groups. Our results suggest that a panel of unique serum metabolites is clinical potential biomarker set for the disease diagnosis and CMS classification for CRC patients. These metabolite markers would give a promise to reflect the essence of the patients with QD or YD. Moreover, the energy metabolism disorder is specially prominent in CRC patients with QD, while the process of protein synthesis is more seriously disordered in those with YD, which is in accordance with the traditional theory of TCM for Qi and Yin deficiency [
To investigate colorectal cancer metabolism, Zhang et al. performed an electronic literature search, from 1998 to January 2016 to evaluate the metabolomic profile of patients with CRC regarding the diagnosis, recurrence, and prognosis/survival and systematically review the twenty-three literatures included [
Metabolomic data typically contains lots of variables, which are interrelated. Multivariate statistical methods such as PCA and OPLS-DA coupled with univariate statistical methods such as Student’s
On the other hand, in this study, 24 metabolic pathways related to 27 discriminating metabolites were found in the QD group compared with the ND group, while 31 metabolic pathways related to 27 discriminating metabolites were found in the YD group compared with the ND group. These results indicate that although more severely metabolic disorder occurs during cancer occurrence and progression in CRC patients with QD, YD influences more metabolic pathways in a weaker level. This phenomenon could offer a possible explanation for the reason why CRC patients with YD were more difficult to treat in some extent [
One of the limitations of our study was insufficient samples. Only 30 samples were included for each group, which is a small number for such a complicated disease and syndrome. A study on a larger scale should be conducted to establish a precise metabolomics diagnostic model.
Ma et al. developed an integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer and 5 individual metabolites and the 5 individual proteins were characterized and their potential for CRC diagnosis was validated [
Traditional Chinese medicine
Chinese medicine syndrome
Colorectal cancer
Qi deficiency
Yin deficiency
Nondeficiency
Carcinoembryonic antigen
Carbohydrate antigen 19-9
Alanine aminotransferase
Aspartate transaminase
Total bilirubin
Direct bilirubin
Serum creatinine
Blood urea nitrogen
Gas chromatography–mass spectrometry
Principal component analysis
Partial least-squares discrimination analysis
Variable Importance in the Projection.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors have declared that no conflicts of interest exist.
Fangfang Tao and Ping Lü contributed equally to this work.
This work is supported by National Natural Science Foundation of China (Fangfang Tao, no. 81302896,