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Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

Abstract

Background

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

Methods

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

Results

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3′4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

Conclusion

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Peer Review reports

Background

Esophageal cancer (EC) as an aggressive malignant tumor, is the sixth leading cause of cancer death globally [1]. Over 50% of all EC-related deaths occur in China where esophageal squamous cell carcinoma (ESCC) is the predominant histologic subtype [2]. Surgery is the primary treatment of esophageal cancer, especially for patients with early stage [3, 4]. But most esophageal cancer cases has progressed to advanced stage before they are finally diagnosed [5]. Neoadjuvant chemoradiotherapy (nCRT) has been considered as a promising therapy strategy for patients with stage II or III esophageal cancer. Several studies have shown that neoadjuvant chemoradiotherapy plus surgery contributes to improved local control, progression free survival, and overall survival compared with surgery alone [6,7,8]. However, not all the EC patients could benefit from nCRT and poor responders have to experience severe toxicity and impaired quality of life [9, 10]. Moreover, the outcomes of non-responders were found to be worse than those underwent primary resection [11]. Hence, it is essential to find a reliable marker of chemoradiosensitivity in esophageal cancer to avoid wasting valuable time and obtain a more favorable prognosis for patients.

Metabolomics have been widely applied in diagnosis and biomarker screening study on various disease, including cancer [12, 13]. Since metabolites represent the end products of biochemical processes, it is closely linked to the overall physiopathological status of an individual [14]. It has been discovered that alterations of metabolites in the biofluids (serum, plasma, and urine) are related to prognosis [15], recurrence [16], treatment response [17] in cancer patients. So far, metabolomic studies on EC have been performed to identify differential metabolite markers between patients and controls [18]. In addition, multiple metabolites have been found to be strongly associated with the degree of tumor progression through metabolomics-based methods [19, 20]. As for the biomarker screening for chemoradiosensitivity in ESCC, one research with small sample size revealed that serum levels of several metabolites differed significantly between the pathological complete response (pCR) group and non-pCR group [21]. However, no other external validation was provided.

In the present study, we aim to investigate the differences in plasma metabolomic characteristics between pCR and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). We used gas chromatography time-of-flight mass spectrometry (GC-TOF/MS) technology which is more conducive to the rapid detection of complex samples analysis for untargeted metabolic profiling. The results showed that five metabolites demonstrated differences between pCR and non-pCR patients in the plasma collected before the onset of neoadjuvant therapy. And pathway analyses unveiled that citrate cycle, glyoxylate and dicarboxylate metabolic pathway were associated between pCR and non-pCR groups. This study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC to neoadjuvant chemoradiotherapy.

Methods

Sample collection

The study included plasma samples from 46 stage II–III esophageal cancer patients who were prospectively selected at the Anyang Cancer Hospital (Henan, China) between June 2017 and April 2019. All patients had been pathologically diagnosed esophageal squamous cell carcinoma. The neoadjuvant chemoradiotherapy (nCRT) consisted of radiotherapy (total radiation dose: 45Gy, 1.8Gy/day, 25 fractions) and concurrent chemotherapy with paclitaxel (135–150 mg/m2) plus cisplatin (50–75 mg/m2) every 21 days for two cycles. 4–6 weeks after completion of nCRT, patients underwent surgery. Clinical stages and pathological stages were determined according to the eighth edition of the American Joint Committee on Cancer tumor-node-metastasis (TNM) staging criteria [22]. All pathology slides were reviewed by a pathologist to determine the pathologic response. pCR was defined as no evidence of viable tumor cells in all specimens, including the primary site and lymph nodes [23]. Samples were collected just before the onset of neoadjuvant therapy and kept frozen and stored at − 80 °C for further analysis. This study was approved by the Ethics Committee of Anyang Cancer Hospital.

Sample preparation for metabolomic analysis

The untargeted metabolomics profiling was implemented on XploreMET platform (Metabo-Profile, Shanghai, China). The sample preparation was conducted as their published methods with minor modifications [24, 25]. In brief, the plasma samples were centrifuged at 3000×g and 4 °C for 5 min (Microfuge 20R, Beckman Coulter, Inc., Indianapolis, IN, USA) after thawing to separate debris or a lipid layer. Metabolites were extracted from plasma samples (50 μL) with 10 μL of internal standard (0.5 mM 4-Chlorophenylalanine) and 175 μL of pre-chilled methanol: chloroform (3:1) followed by centrifugation at 14, 000×g for 20 min at 4 °C. Then each 200 μL of the supernatant was transferred into an autosampler vial (Agilent Technologies, Foster City, CA, USA). The resting supernatant from each sample was pooled to prepare quality control samples. Following solvent evaporation and lyophilization, the dried samples were derivatized with 50 μL of methoxyamine (20 mg/ml in pyridine) for 2 h, followed by silylanization with 50 μL of MSTFA (1% TMCS) for 1 h prior to injection. Above two steps were performed by a robotic multipurpose sample MPS2 with dual heads (Gerstel, Muehlheim, Germany).

Metabolomic analysis

The GC-TOF/MS analysis was performed using a time-of-flight mass spectrometry (GC-TOF/MS) system (Pegasus HT, Leco Corp., St. Joseph, MO,USA),which consists of an Agilent 7890B gas chromatography and a Gerstel multipurpose sample MPS2 with dual heads (Gerstel, Muehlheim, Germany). As described previously [26], DB-5MS GC column (30 m × 250 μm i.d., 0.25-μm film thickness; Restek corporation, Bellefonte, PA, USA) was chosen for separation. Helium was used as the carrier gas at a steady flow rate of 1.0 mL/min. The temperature of transfer interface and injection were both 270 °C. The source temperature was set as 220 °C. The measurements were taken using electron impact ionization (70 eV) in the full scan mode (m/z 50–500).

Data processing

XploreMET (v3.0, Metabo-Profile, Shanghai, China) was used to process the raw data generated by GC-TOF/MS. The data processing includes baseline denosing and smoothing, peak picking and deconvultion, creating reference database from the pooled QC samples, metabolite signal alignment, missing value correction and imputation, and QC correction as previously reported [27]. Metabolites were identified by comparing both retention index and mass spectral data with JiaLibTM metabolite database. Each data set was converted into comparable data vectors for statistical analysis. The metabolites with t test or U test P <0.05 by unidimensional analysis were considered as differentially expressed between pCR and non-pCR groups.

Pathway analyses

To further investigate the metabolic pathways involved in the chemoradiosensitivity in ESCC, the differential metabolites were annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) and Human Metabolome Database (HMDB, http://www.hmdb.ca/). MetaboAnalyst 4.0 (http://www. metaboanalyst.ca/MetaboAnalyst/) were then applied to analyze the data by R software (v3.4.3, GitHub). The iPath 3.0 (http://pathways.embl.de/) was used to show the metabolic network of the differential metabolites and altered metabolic pathways in KEGG general metabolic pathway.

Statistical analysis

All measurements were mean-centered and scaled by the standard deviation of the observed measurements. Fisher’s exact probability test, the Student’s t-test, or the Mann–Whitney U-test was used to evaluate the statistical significance of differences. The Mann–Whitney U test was utilized for non-normal distribution data and the Student’s t-test was applied for normal distribution data. Fisher’s exact tests for categorical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was performed to assess the feasibility of using the plasma levels of particular metabolites as predictive biomarkers. The data was randomly split into a training set (data from 36 patients) and a test set (data from 10 patients). Predictions were made on the test set based on the Gaussian Naive Bayes model trained in the training set. P < 0.05 was considered statistically significant.

Results

Patients & treatment outcomes

A total of 46 patients were included in this study. The age of all subjects ranged from 50 to 84 years, including 30 men and 16 women. According to the American Joint Committee on Cancer tumor-node-metastasis (TNM) staging criteria (the eighth edition), the pre-treatment clinical stages of these 46 subjects were II (n = 4) and III (n = 42), respectively. After nCRT, 23 patients achieved pCR through the examination of surgical pathology specimen, while the remaining 23 patients were identified into non-pCR group. The baseline characteristics of the ESCC patients and treatment outcomes are shown in Table 1. According to Fisher’s exact probability test, tumor location and clinical stage are significantly different between the pCR and non-pCR groups. There were no significant differences in gender, age, smoking and alcohol consumption between the two groups.

Table 1 Clinical characteristics of the patients

Altered metabolites in plasma among pCR and non- pCR patients

A total of 109 metabolites of different classes (Amino Acids, Organic Acids, Carbohydrates, Phosphates, Fatty Acids, Indoles) were detected in the plasma samples from 46 subjects using untargeted metabolomics analysis (Fig. 1a and Supplementary List 1). To further determine whether the metabolite footprints in plasma differed between pCR and non-pCR subjects, unidimensional analysis was used (Student’s t-test or Mann–Whitney U test was selected according to the data) to obtain the differential metabolites between the two groups. We found five differentially expressed metabolites (out of 109) in plasma levels between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid, linoleic acid, citric acid were upregulated (p = 0.0129, 0.0137 and 0.0473, respectively), and L-histidine, 3′4-Dihydroxyhydrocinnamic acid were downregulated (p = 0. 0.0155 and 0.0339, respectively) in the pCR plasma samples (Fig. 1b-f). In order to display the relationships among samples and differences in expression patterns more intuitively, we also conducted the bidirectional clustering analysis of each samples and the significantly different metabolites. As the results showed that the samples in two groups can be distinguished, and metabolites between two groups existed difference (Fig. 1g).

Fig. 1
figure1

Altered metabolites in plasma among pCR and non-pCR patients. a Pie chart illustrating the abundance ratio of different classes of metabolites detected by untargeted metabolic profiling in plasma samples from ESCC patients. b-f Five identified metabolites that differed significantly between pCR and non-pCR groups using unidimensional analysis. g Hierarchical clustering of significantly different metabolites. The tree structure on the left side represents the clustering relationships of each metabolite, and the tree structure at the top represents the clustering relationships of each sample

Furthermore, ROC analysis was applied to explore whether the five differential metabolites could be converted into an approach for predicting nCRT response. As the ROC curve performed by Gaussian Naive Bayes model showed that the area under the curve (AUC) value was 0.76 (Fig. 2), which implied a good ability in predicting the response of pCR and non-pCR of esophageal cancer patients by these five metabolites.

Fig. 2
figure2

Evaluation of the five differential metabolites in predictive precision. a ROC curve based on the five differentially expressed plasma metabolites. b Confusion matrix showing classification results (80% overall correct assignment)

Metabolic pathway analysis

To further explore the metabolic pathways related to above ESCC chemoradiosensitivity-associated metabolites, MetaboAnalyst was used to indicate the metabolic pathways connected with these five metabolites. Through pathway analysis based on KEGG database, two pathways namely citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism were altered in pCR patients (Fig. 3 and Table 2). The overall metabolic network of the altered metabolites and metabolic pathways is shown in Supplementary Figure 1.

Fig. 3
figure3

Pathway analysis of the differential metabolites between the pCR and non-pCR groups. The altered pathway of ESCC chemoradiosensitivity-associated metabolites. The color and size of each circle is based on P value and pathway impact value respectively

Table 2 Pathway analysis of metabolite changes

Discussion

Although the benefit of nCRT has been demonstrated in multiple trials, only approximately 20–25% of EC patients showed complete response, and some even developed resistance to currently used therapy [28]. Chemoradiotherapy resistance is a considerable obstacle to the effective treatment of esophageal cancer. Unfortunately, we have not yet identified specific biomarkers in blood which would reliably and accurately predict response to nCRT.

Metabolism alterations in cancer are well-documented. Metabolomics as a new high throughput technology provides a broad field for finding metabolites as potential biomarkers in body fluids for diagnosis, treatment and prognosis of cancer. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) with its pivotal characteristics, including higher selectivity, resolution, sensitivity and accuracy is well-suited for the identification and quantitation of low molecular weight metabolites [29, 30]. In the present study, GC-TOF/MS-based untargeted metabonomics approach was utilized to profile metabolites in ESCC plasma samples from the pCR and non-pCR patients.

The major findings of this study were summarized as follows: i) A total of five key metabolites associated with ESCC chemoradiosensitivity were identified; ii) Among the five differential metabolites, isocitric acid, linoleic acid and citric acid were upregulated, while L-histidine, 3′4 dihydroxyhydrocinnamic acid were downregulated (Fig. 1b-f); iii) Multiple metabolic pathways, especially citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism were significantly altered between pCR and non-pCR (Fig. 3 and Table 2).

In the previous study, altered metabolic profile was found between the pCR and non-pCR ESCC patients in the serum level detected by gas chromatography mass spectrometry (GC/MS) analysis and liquid chromatography mass spectrometry (LC/MS) analysis [21]. But only three differential metabolites (arabitol, uracil and 3-aminoglutaric acid) were detected by GC/MS analysis. While in the present research, five differential metabolites, including citric acid and isocitric acid were identified as plasma biomarker candidates for chemoradiosensitivity applied by GC-TOF/MS analysis. There is no evidence that plasma or serum was superior to be applied in clinical metabolomic study. However, there are indeed differential distributions of specific metabolites between plasma and serum. For instance, erythritol, glycerophosphocholines, glutamine, creatinine, and hexadecanoic acid in plasma, but not in serum, were shown to link with life expectancy for small-cell lung cancer patients [31]. In addition, another reason including chemical diversity and pretreatment of sample may also have impact on the disparate results. For example, citrate and citrate cycle metabolites were found in relative lower value in serum, partially because citrate in serum forms divalent cation complexes, which will precipitate by methanol extraction method [32]. Our study provided another dimension in investigation of the metabolites associated with nCRT respondence in the plasma level.

In the present study, L-histidine was found downregulated in the pCR patients. Previous study showed that increasing serum level of histidine was observed in breast cancer patient and strongly related with disease relapse [33]. Moreover, histidine degradation was found to be able to enhance the sensitivity to cancer therapy [34]. Meanwhile, recent study reported that high expression of FAM83A predicted a poor prognosis in lung adenocarcinoma patients, and histidine metabolism pathway was found significantly activated in FAM83A high expressed lung adenocarcinoma sample [35]. It is considered that the marked gain of histidine may enhance the total antioxidant and metal-binding capacity of the proteome of the cancer cell and thus potentially serve as a nonspecific compensatory mechanism to relieve consequences of the cancer-related aggravation of oxidative stress [36]. Furthermore, according to the pathway analysis, the tricarboxylic acid (TCA) cycle and the glyoxylate and dicarboxylate metabolism were found significantly related to ESCC chemoradiosensitivity. The TCA cycle is a key process for energy generation, which consumes oxygen and generates high amounts of ATP through oxidative phosphorylation. While for cancer cells, glycolysis is the primary pathway for energy production even if oxygen is available. Glycolytic pyruvate prefers to be converted into lactate, rather than enter into the mitochondrial TCA cycle. This phenomenon is called aerobic glycolysis or the Warburg effect. This kind of aberration has been proved to afford biosynthetic precursors for rapid macromolecule synthesis, and to keep cellular redox homeostasis for better survival [37, 38]. Mounting studies have demonstrated that there is a relationship between aerobic glycolysis and the occurrence of tumor drug resistance. And several key proteins in the glycolytic pathway have been discovered as promising targets for overcoming chemoresistance [39,40,41]. It was reported that suppressing glycolytic enzymes by inhibiting RAC1 showed reduced cisplatin resistance in esophageal squamous cell carcinoma [42]. Meanwhile, pretreatment whole-body total lesion glycolysis was uncovered as an independent predictor of outcomes in patients with esophageal cancer treated with definitive chemoradiotherapy [43]. In addition, altered glyoxylate and dicarboxylate metabolic pathway have been reported in various cancer. Evidence showed that glyoxylate and dicarboxylate metabolism was associated with the loss of tumor cell differentiation in lung adenocarcinomas [44]. Current study suggested that glyoxylate and dicarboxylate metabolism combined with other pathways could distinguish tumor from normal tissues in colorectal cancer [45]. In gastric cancer, glyoxylate and dicarboxylate metabolism were observed in the chromosomal instability type alone [46]. While previous study demonstrated that chromosomal instability is a favorable predictor of response to cisplatin-based neoadjuvant chemotherapy in patients [47]. In combination with the findings in this study, citric acid and isocitric acid, as the intermediates of the TCA cycle and the glyoxylate and dicarboxylate metabolic pathway, were up-regulated in the pCR patients. It is suggested that the energy metabolism was involved in the regulatory mechanism of sensitivity to neoadjuvant therapy for esophageal cancer.

There also exist some limitations in this study. The primary limitation is that this is a single–center study with relatively small number of subjects, making the data less conclusive. In addition, due to the restriction of the conditions, we only applied one metabolomics method to identify the potential changed metabolites. Furthermore, the predictive precision of those differential metabolites needed external validation. Larger samples and a combination of research methods may help us better understand the mechanism of ESCC chemoradiotherapy resistance.

Conclusion

Overall, we found that significant alteration of metabolites between the responders and non-responders ESCC patients who received neoadjuvant chemoradiotherapy. The pCR group exhibited higher level of isocitric acid, linoleic acid, citric acid, and lower level of L-histidine, 3′4 dihydroxyhydrocinnamic acid than the non-pCR group. Changes in plasma metabolic signature may reflect reprogramming of the aforementioned metabolic pathways. Further study is needed to validate these findings using larger samples and to explore the underlying mechanism of ESCC chemoradiotherapy resistance.

Availability of data and materials

All the necessary materials can be found in the text or supplementary materials. Due to the privacy policy, the confidential data materials could only be obtained with the permission of the corresponding authors.

Abbreviations

AUC:

Area under the curve

EC:

Esophageal cancer

ESCC:

Esophageal squamous cell carcinoma

GC-TOF/MS:

Gas chromatography time-of-flight mass spectrometry

GC/MS:

Gas chromatography mass spectrometry

LC/MS:

Liquid chromatography mass spectrometry

nCRT:

Neoadjuvant chemoradiotherapy

pCR:

Pathological complete response

ROC:

Receiver operating characteristic

TCA:

Tricarboxylic acid

TNM:

Tumor-node-metastasis

References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Shen FF, Pan Y, Yang HJ, Li JK, Zhao F, Su JF, et al. Decreased expression of SPINT1-AS1 and SPINT1 mRNA might be independent unfavorable prognostic indicators in esophageal squamous cell carcinoma. OncoTargets Ther. 2019;12:4755–63.

    CAS  Google Scholar 

  3. 3.

    Chen MF, Chen PT, Lu MS, Lee CP, Chen WC. Survival benefit of surgery to patients with esophageal squamous cell carcinoma. Sci Rep. 2017;7:46139.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    D'Amico TA. Outcomes after surgery for esophageal cancer. Gastrointestinal Cancer Res. 2007;1:188–96.

    Google Scholar 

  5. 5.

    Meves V, Behrens A, Pohl J. Diagnostics and early diagnosis of esophageal Cancer. Viszeralmedizin. 2015;31:315–8.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Shapiro J, van Lanschot JJB, Hulshof M, van Hagen P, van Berge Henegouwen MI, Wijnhoven BPL, et al. Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS): long-term results of a randomised controlled trial. Lancet Oncol. 2015;16:1090–8.

    Google Scholar 

  7. 7.

    Yang H, Liu H, Chen Y, Zhu C, Fang W, Yu Z, et al. Neoadjuvant Chemoradiotherapy followed by surgery versus surgery alone for locally advanced squamous cell carcinoma of the esophagus (NEOCRTEC5010): a phase III multicenter, randomized, open-label clinical trial. J Clin Oncol. 2018;36:2796–803.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    van Hagen P, Hulshof MC, van Lanschot JJ, Steyerberg EW, van Berge Henegouwen MI, Wijnhoven BP, et al. Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med. 2012;366:2074–84.

    Google Scholar 

  9. 9.

    Sato Y, Motoyama S, Saito H, Minamiya Y. Novel candidate biomarkers of Chemoradiosensitivity in esophageal squamous cell carcinoma: a systematic review. Eur Surg Res. 2016;56:141–53.

    CAS  PubMed  Google Scholar 

  10. 10.

    Borggreve AS, Mook S, Verheij M, Mul VEM, Bergman JJ, Bartels-Rutten A, et al. Preoperative image-guided identification of response to neoadjuvant chemoradiotherapy in esophageal cancer (PRIDE): a multicenter observational study. BMC Cancer. 2018;18:1006.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hsu PK, Chien LI, Huang CS, Hsieh CC, Wu YC, Hsu WH, et al. Comparison of survival among neoadjuvant chemoradiation responders, non-responders and patients receiving primary resection for locally advanced oesophageal squamous cell carcinoma: does neoadjuvant chemoradiation benefit all? Interact Cardiovasc Thorac Surg. 2013;17:460–6.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Mamas M, Dunn WB, Neyses L, Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol. 2011;85:5–17.

    CAS  PubMed  Google Scholar 

  13. 13.

    Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res. 2009;15:431–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Puchades-Carrasco L, Pineda-Lucena A. Metabolomics applications in precision medicine: an oncological perspective. Curr Top Med Chem. 2017;17:2740–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Qiu Y, Cai G, Zhou B, Li D, Zhao A, Xie G, et al. A distinct metabolic signature of human colorectal cancer with prognostic potential. Clin Cancer Res. 2014;20:2136–46.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Alberice JV, Amaral AF, Armitage EG, Lorente JA, Algaba F, Carrilho E, et al. Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry metabolomics approach. J Chromatogr A. 2013;1318:163–70.

    CAS  PubMed  Google Scholar 

  17. 17.

    Mock A, Zschabitz S, Kirsten R, Scheffler M, Wolf B, Herold-Mende C, et al. Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers. Cancer Immunol Immunother. 2019;68:2005–14.

    CAS  PubMed  Google Scholar 

  18. 18.

    Davis VW, Schiller DE, Eurich D, Sawyer MB. Urinary metabolomic signature of esophageal cancer and Barrett's esophagus. World J Surg Oncol. 2012;10:271.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Tokunaga M, Kami K, Ozawa S, Oguma J, Kazuno A, Miyachi H, et al. Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. Int J Oncol. 2018;52:1947–58.

    CAS  PubMed  Google Scholar 

  20. 20.

    Buas MF, Gu H, Djukovic D, Zhu J, Onstad L, Reid BJ, et al. Candidate serum metabolite biomarkers for differentiating gastroesophageal reflux disease, Barrett's esophagus, and high-grade dysplasia/esophageal adenocarcinoma. Metabolomics. 2017;13(3). https://doi.org/10.1007/s11306-016-1154-y.

  21. 21.

    Fujigaki S, Nishiumi S, Kobayashi T, Suzuki M, Iemoto T, Kojima T, et al. Identification of serum biomarkers of chemoradiosensitivity in esophageal cancer via the targeted metabolomics approach. Biomark Med. 2018;12:827–40.

    CAS  PubMed  Google Scholar 

  22. 22.

    Rice TW, Ishwaran H, Ferguson MK, Blackstone EH, Goldstraw P. Cancer of the esophagus and Esophagogastric junction: an eighth edition staging primer. J Thoracic Oncol. 2017;12:36–42.

    Google Scholar 

  23. 23.

    Chiu CH, Zhang P, Chang AC, Derstine BA, Ross BE, Enchakalody B, et al. Morphomic factors associated with complete response to Neoadjuvant therapy in esophageal carcinoma. Ann Thorac Surg. 2020;109:241–8.

    PubMed  Google Scholar 

  24. 24.

    Wang Y, Zhao H, Liu Y, Guo W, Bao Y, Zhang M, et al. GC-MS-based metabolomics to reveal the protective effect of gross Saponins of Tribulus terrestris fruit against ischemic stroke in rat. Molecules. 2019;24. https://doi.org/10.3390/molecules24040793.

  25. 25.

    Xi Y, Yuefen P, Wei W, Quan Q, Jing Z, Jiamin X, et al. Analysis of prognosis, genome, microbiome, and microbial metabolome in different sites of colorectal cancer. J Transl Med. 2019;17:353.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Yu Y, Gao Z, Lou J, Mao Z, Li K, Chu C, et al. Identification of serum-based metabolic feature and characteristic metabolites in Paraquat intoxicated mouse models. Front Physiol. 2020;11:65.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Ping F, Guo Y, Cao Y, Shang J, Yao S, Zhang J, et al. Metabolomics analysis of the renal cortex in rats with acute kidney injury induced by sepsis. Front Mol Biosci. 2019;6:152.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Francis AM, Sepesi B, Correa AM, Blum MA, Erasmus JJ, Lee JH, et al. The influence of histopathologic tumor viability on long-term survival and recurrence rates following neoadjuvant therapy for esophageal adenocarcinoma. Ann Surg. 2013;258:500–7.

    PubMed  Google Scholar 

  29. 29.

    Jaeger C, Meret M, Schmitt CA, Lisec J. Compound annotation in liquid chromatography/high-resolution mass spectrometry based metabolomics: robust adduct ion determination as a prerequisite to structure prediction in electrospray ionization mass spectra. Rapid Commun Mass Spectrom. 2017;31:1261–6.

    CAS  PubMed  Google Scholar 

  30. 30.

    Yin S, Guo P, Hai D, Xu L, Shu J, Zhang W, et al. Optimization of GC/TOF MS analysis conditions for assessing host-gut microbiota metabolic interactions: Chinese rhubarb alters fecal aromatic amino acids and phenol metabolism. Anal Chim Acta. 2017;995:21–33.

    CAS  PubMed  Google Scholar 

  31. 31.

    Wedge DC, Allwood JW, Dunn W, Vaughan AA, Simpson K, Brown M, et al. Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer. Anal Chem. 2011;83:6689–97.

    CAS  PubMed  Google Scholar 

  32. 32.

    Dettmer K, Almstetter MF, Appel IJ, Nurnberger N, Schlamberger G, Gronwald W, et al. Comparison of serum versus plasma collection in gas chromatography--mass spectrometry-based metabolomics. Electrophoresis. 2010;31:2365–73.

    CAS  PubMed  Google Scholar 

  33. 33.

    Tenori L, Oakman C, Morris PG, Gralka E, Turner N, Cappadona S, et al. Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study. Mol Oncol. 2015;9:128–39.

    CAS  PubMed  Google Scholar 

  34. 34.

    Kanarek N, Keys HR, Cantor JR, Lewis CA, Chan SH, Kunchok T, et al. Histidine catabolism is a major determinant of methotrexate sensitivity. Nature. 2018;559:632–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Yu J, Hou M, Pei T. FAM83A is a prognosis signature and potential oncogene of lung adenocarcinoma. DNA Cell Biol. 2020;39:890–9.

    CAS  PubMed  Google Scholar 

  36. 36.

    Tsuber V, Kadamov Y, Brautigam L, Berglund UW, Helleday T. Mutations in Cancer cause gain of cysteine, Histidine, and tryptophan at the expense of a net loss of arginine on the proteome level. Biomolecules. 2017;7. https://doi.org/10.3390/biom7030049.

  37. 37.

    Sun L, Suo C, Li ST, Zhang H, Gao P. Metabolic reprogramming for cancer cells and their microenvironment: beyond the Warburg effect. Biochim Biophys Acta Rev Cancer. 1870;2018:51–66.

    Google Scholar 

  38. 38.

    Pavlova NN, Thompson CB. The emerging hallmarks of Cancer metabolism. Cell Metab. 2016;23:27–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Sfakianaki M, Papadaki C, Tzardi M, Trypaki M, Manolakou S, Messaritakis I, et al. PKM2 expression as biomarker for resistance to oxaliplatin-based chemotherapy in colorectal cancer. Ann Oncol. 2019;30 Suppl 4:iv21–iv2.

    Google Scholar 

  40. 40.

    Avolio R, Matassa DS, Criscuolo D, Landriscina M, Esposito F. Modulation of mitochondrial metabolic reprogramming and oxidative stress to overcome Chemoresistance in Cancer. Biomolecules. 2020;10. https://doi.org/10.3390/biom10010135.

  41. 41.

    Abdel-Wahab AF, Mahmoud W, Al-Harizy RM. Targeting glucose metabolism to suppress cancer progression: prospective of anti-glycolytic cancer therapy. Pharmacol Res. 2019;150:104511.

    CAS  PubMed  Google Scholar 

  42. 42.

    Zeng RJ, Zheng CW, Gu JE, Zhang HX, Xie L, Xu LY, et al. RAC1 inhibition reverses cisplatin resistance in esophageal squamous cell carcinoma and induces downregulation of glycolytic enzymes. Mol Oncol. 2019;13:2010–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Takahashi N, Umezawa R, Takanami K, Yamamoto T, Ishikawa Y, Kozumi M, et al. Whole-body total lesion glycolysis is an independent predictor in patients with esophageal cancer treated with definitive chemoradiotherapy. Radiother Oncol. 2018;129:161–5.

    CAS  PubMed  Google Scholar 

  44. 44.

    Creighton C, Hanash S, Beer D. Gene expression patterns define pathways correlated with loss of differentiation in lung adenocarcinomas. FEBS Lett. 2003;540:167–70.

    CAS  PubMed  Google Scholar 

  45. 45.

    Arima K, Lau MC, Zhao M, Haruki K, Kosumi K, Mima K, et al. Metabolic profiling of formalin-fixed paraffin-embedded tissues discriminates Normal Colon from colorectal Cancer. Mol Cancer Res. 2020;18:883–90.

    PubMed  Google Scholar 

  46. 46.

    Tsai CK, Yeh TS, Wu RC, Lai YC, Chiang MH, Lu KY, et al. Metabolomic alterations and chromosomal instability status in gastric cancer. World J Gastroenterol. 2018;24:3760–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Ott K, Vogelsang H, Mueller J, Becker K, Muller M, Fink U, et al. Chromosomal instability rather than p53 mutation is associated with response to neoadjuvant cisplatin-based chemotherapy in gastric carcinoma. Clin Cancer Res. 2003;9:2307–15.

    CAS  PubMed  Google Scholar 

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Acknowledgments

We thank all of patients for their participation in the study and their doctors for their willingness to refer patients.

Funding

This study was supported by National Natural Science Foundation of China (U1504814), Major projects of science and Technology Department in Henan Province (16110311200, 161100311300) and Anyang Science Foundation of Henan province (2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors

Contributions

FZ, JHZ and YZ conceived and designed the study. YZ, JW, ND, PH and JL collected the clinical data. YZ, JW and ND performed the experiments. WY and JMZ analyzed the data. JHZ and YZ wrote the manuscript. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Jiahuan Zhou or Fuyou Zhou.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Anyang Cancer Hospital, Chinese Academy of Medical Sciences, and all human subjects signed informed consent before participating in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no conflicts of interest.

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Supplementary information

Additional file 1.

Additional file 2: Supplementary Figure 1.

Metabolic network of the changed metabolites and altered metabolic pathways in KEGG general metabolic pathway map. Red dots represent the increased metabolites in pCR group; Blue dots represent the specifically decreased metabolites in pCR group.

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Zhang, Y., Wang, J., Dai, N. et al. Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach. BMC Cancer 20, 835 (2020). https://doi.org/10.1186/s12885-020-07336-9

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Keywords

  • Chemoradiosensitivity
  • Esophageal squamous cell carcinoma
  • Metabolomics
  • Neoadjuvant therapy
  • Untargeted metabolomics analysis