Lijia Dong

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

PURPOSE: Aurora-A/STK15/BTAK, a centrosome-associated oncogenic protein, is implicated in the control of mitosis. Overexpression of Aurora-A has been shown to result in chromosomal aberration and genomic instability. Multiple lines of evidence indicate that Aurora-A induces cell malignant transformation. In the current study, we are interested in investigating the expression of Aurora-A in human esophageal squamous cell carcinoma (ESCC) and characterizing the association of Aurora-A with ESCCmalignant progression. EXPERIMENTAL DESIGN: Aurora-A protein expression was examined in 84 ESCC tissues and 81 paired normal adjacent tissues by either immunohistochemistry or Western blot analysis. In addition, a gene-knockdown small interfering RNA technique was used in ESCC cells to investigate whether Aurora-A contributes to the ability of a tumor to grow invasively. RESULTS: The amount of Aurora-A protein in ESCC was considerably higher than that in normal adjacent tissues. Overexpression of Aurora-A was observed in 57 of 84 (67.5%) ESCC samples. In contrast, <2% of normal adjacent tissue displayed high expression of Aurora-A. Interestingly, overexpression of Aurora-A seemed to correlate with the invasive malignancy of ESCC. Disruption of endogenous Aurora-A using small interfering RNA technique substantially suppressed cell migrating ability. CONCLUSION: The findings presented in this report show that Aurora-A expression is elevated in human esophageal squamous cell carcinoma and is possibly associated with tumor invasion, indicating that overexpression of Aurora-A may contribute to ESCC occurrence and progression.

Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre- versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, l-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC) 1The abbreviations used are:ROCReceiver Operating CharacteristicAUCArea Under the Receiver Operating Characteristic CurveBMIBody Mass IndexCRTChemoradiotherapyECEsophageal CarcinomaESCCEsophageal Squamous Cell CarcinomaFAFormic AcidLysoPCsLysophosphatidylcholinesMVDAMultivariate Statistical Data AnalysisNCNormal ControlOROverall ResponderNon-ORNonoverall ResponderOPLS-DAOrthogonal Partial Least Square Discriminant AnalysisPCAPrincipal Component AnalysisPLS-DAPartial Least Squares Discriminant AnalysisQCQuality ControlRRLCRapid Resolution Liquid ChromatographyRRLC-(±)ESI-MSRRLC-MS Analysis by ESI in Both Positive and Negative Ion ModesRRLC-(+)ESI-MSRRLC-MS Analysis by ESI in Positive Ion ModeRRLC–(−)ESI-MSRRLC-MS Analysis by ESI in Negative Ion ModeTICTotal Ion ChromatogramXICExtracted Ion ChromatogramTNMTumor Nodes MetastasisVIPVariable Importance in Projection ValuesSRMSelected Reaction MonitoringCECollision EnergyMS/MSTandem Mass SpectrometryRRLC-MS/MS SRMRapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. 1The abbreviations used are:ROCReceiver Operating CharacteristicAUCArea Under the Receiver Operating Characteristic CurveBMIBody Mass IndexCRTChemoradiotherapyECEsophageal CarcinomaESCCEsophageal Squamous Cell CarcinomaFAFormic AcidLysoPCsLysophosphatidylcholinesMVDAMultivariate Statistical Data AnalysisNCNormal ControlOROverall ResponderNon-ORNonoverall ResponderOPLS-DAOrthogonal Partial Least Square Discriminant AnalysisPCAPrincipal Component AnalysisPLS-DAPartial Least Squares Discriminant AnalysisQCQuality ControlRRLCRapid Resolution Liquid ChromatographyRRLC-(±)ESI-MSRRLC-MS Analysis by ESI in Both Positive and Negative Ion ModesRRLC-(+)ESI-MSRRLC-MS Analysis by ESI in Positive Ion ModeRRLC–(−)ESI-MSRRLC-MS Analysis by ESI in Negative Ion ModeTICTotal Ion ChromatogramXICExtracted Ion ChromatogramTNMTumor Nodes MetastasisVIPVariable Importance in Projection ValuesSRMSelected Reaction MonitoringCECollision EnergyMS/MSTandem Mass SpectrometryRRLC-MS/MS SRMRapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre- and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC. Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre- versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, l-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC) 1The abbreviations used are:ROCReceiver Operating CharacteristicAUCArea Under the Receiver Operating Characteristic CurveBMIBody Mass IndexCRTChemoradiotherapyECEsophageal CarcinomaESCCEsophageal Squamous Cell CarcinomaFAFormic AcidLysoPCsLysophosphatidylcholinesMVDAMultivariate Statistical Data AnalysisNCNormal ControlOROverall ResponderNon-ORNonoverall ResponderOPLS-DAOrthogonal Partial Least Square Discriminant AnalysisPCAPrincipal Component AnalysisPLS-DAPartial Least Squares Discriminant AnalysisQCQuality ControlRRLCRapid Resolution Liquid ChromatographyRRLC-(±)ESI-MSRRLC-MS Analysis by ESI in Both Positive and Negative Ion ModesRRLC-(+)ESI-MSRRLC-MS Analysis by ESI in Positive Ion ModeRRLC–(−)ESI-MSRRLC-MS Analysis by ESI in Negative Ion ModeTICTotal Ion ChromatogramXICExtracted Ion ChromatogramTNMTumor Nodes MetastasisVIPVariable Importance in Projection ValuesSRMSelected Reaction MonitoringCECollision EnergyMS/MSTandem Mass SpectrometryRRLC-MS/MS SRMRapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. 1The abbreviations used are:ROCReceiver Operating CharacteristicAUCArea Under the Receiver Operating Characteristic CurveBMIBody Mass IndexCRTChemoradiotherapyECEsophageal CarcinomaESCCEsophageal Squamous Cell CarcinomaFAFormic AcidLysoPCsLysophosphatidylcholinesMVDAMultivariate Statistical Data AnalysisNCNormal ControlOROverall ResponderNon-ORNonoverall ResponderOPLS-DAOrthogonal Partial Least Square Discriminant AnalysisPCAPrincipal Component AnalysisPLS-DAPartial Least Squares Discriminant AnalysisQCQuality ControlRRLCRapid Resolution Liquid ChromatographyRRLC-(±)ESI-MSRRLC-MS Analysis by ESI in Both Positive and Negative Ion ModesRRLC-(+)ESI-MSRRLC-MS Analysis by ESI in Positive Ion ModeRRLC–(−)ESI-MSRRLC-MS Analysis by ESI in Negative Ion ModeTICTotal Ion ChromatogramXICExtracted Ion ChromatogramTNMTumor Nodes MetastasisVIPVariable Importance in Projection ValuesSRMSelected Reaction MonitoringCECollision EnergyMS/MSTandem Mass SpectrometryRRLC-MS/MS SRMRapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre- and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC. Receiver Operating Characteristic Area Under the Receiver Operating Characteristic Curve Body Mass Index Chemoradiotherapy Esophageal Carcinoma Esophageal Squamous Cell Carcinoma Formic Acid Lysophosphatidylcholines Multivariate Statistical Data Analysis Normal Control Overall Responder Nonoverall Responder Orthogonal Partial Least Square Discriminant Analysis Principal Component Analysis Partial Least Squares Discriminant Analysis Quality Control Rapid Resolution Liquid Chromatography RRLC-MS Analysis by ESI in Both Positive and Negative Ion Modes RRLC-MS Analysis by ESI in Positive Ion Mode RRLC-MS Analysis by ESI in Negative Ion Mode Total Ion Chromatogram Extracted Ion Chromatogram Tumor Nodes Metastasis Variable Importance in Projection Values Selected Reaction Monitoring Collision Energy Tandem Mass Spectrometry Rapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. Receiver Operating Characteristic Area Under the Receiver Operating Characteristic Curve Body Mass Index Chemoradiotherapy Esophageal Carcinoma Esophageal Squamous Cell Carcinoma Formic Acid Lysophosphatidylcholines Multivariate Statistical Data Analysis Normal Control Overall Responder Nonoverall Responder Orthogonal Partial Least Square Discriminant Analysis Principal Component Analysis Partial Least Squares Discriminant Analysis Quality Control Rapid Resolution Liquid Chromatography RRLC-MS Analysis by ESI in Both Positive and Negative Ion Modes RRLC-MS Analysis by ESI in Positive Ion Mode RRLC-MS Analysis by ESI in Negative Ion Mode Total Ion Chromatogram Extracted Ion Chromatogram Tumor Nodes Metastasis Variable Importance in Projection Values Selected Reaction Monitoring Collision Energy Tandem Mass Spectrometry Rapid Resolution Liquid Chromatography-Tandem Mass Spectrometry in Selected Reaction Monitoring Mode. Worldwide, esophageal cancer (EC) is the eighth most prevalent cancer, and it is also one of the most lethal, accounting for more than 300,000 deaths per year. There are two major histological types of EC, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), of which ESCC is dominant globally (1Parkin D.M. Bray F. Ferlay J. Pisani P. Global Cancer Statistics, 2002.CA Cancer J. Clin. 2005; 55: 74-108Crossref PubMed Scopus (17303) Google Scholar, 2Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global Cancer Statistics.CA Cancer J. Clin. 2011; 61: 69-90Crossref PubMed Scopus (30255) Google Scholar, 3Vizcaino A.P. Moreno V. Lambert R. Parkin D.M. Time trends incidence of both major histologic types of esophageal carcinomas in selected countries, 1973–1995.Int. J. Cancer. 2002; 99: 860-868Crossref PubMed Scopus (362) Google Scholar). Massive studies have revealed the prevalence of this disease in China (1Parkin D.M. Bray F. Ferlay J. Pisani P. Global Cancer Statistics, 2002.CA Cancer J. Clin. 2005; 55: 74-108Crossref PubMed Scopus (17303) Google Scholar, 2Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global Cancer Statistics.CA Cancer J. Clin. 2011; 61: 69-90Crossref PubMed Scopus (30255) Google Scholar, 4Yang C.S. Research on esophageal cancer in China.Cancer Res. 1980; 40: 2633-2644PubMed Google Scholar). However, the current diagnostic, screening, and surveillance methods for EC such as upper gastrointestinal endoscopy, barium swallows, and serology markers etc. (5Wakatsuki M. Suzuki Y. Nakamoto S. Ohno T. Ishikawa H. Kiyohara H. Kiyozuka M. Shirai K. Nakayama Y. Nakano T. Clinical usefulness of CYFRA 21–1 for esophageal squamous cell carcinoma in radiation therapy.J. Gastroenterol. Hepatol. 2007; 22: 715-719PubMed Google Scholar), specific in chemoradiotherapy (CRT) is as one of the most current for EC T. T. M. T. Y. D. T. K. T. M. T. of of chemoradiotherapy for esophageal squamous cell carcinoma in J. Clin. 2007; PubMed Scopus Google Scholar, A. M. K. T. T. K. T. Y. T. K. T. of of on of in patients with esophageal squamous cell J. PubMed Scopus Google Scholar, A. M. K. T. T. T. Y. T. K. T. of with in a chemoradiotherapy in patients with esophageal squamous cell J. PubMed Scopus Google Scholar). However, as with in to is and this a major on biomarkers EC responses to therapeutic are to and as well as for monitoring therapeutic responses and predicting a in Scholar, E. the metabolic responses of to multivariate statistical analysis of biological PubMed Scopus Google Scholar, and to metabolic PubMed Scopus Google Scholar), to be a to global in the metabolic profiles of in to disease treatment of cancer two a biomarker 2011; PubMed Scopus Google Scholar, H. E. S. E. Rapid and diagnosis of the and of disease using based 2002; PubMed Google Scholar, J. Y. M.M. Y. P. and potential biomarkers of 2011; PubMed Scopus Google Scholar, A. A.P. R. Y. A. S. J. D. S. A. S. profiles potential for in cancer PubMed Scopus Google Scholar, S. J. K. biomarkers revealed by metabolomics Res. PubMed Scopus Google Scholar). metabolomics an for the of and therapeutic that of the may have potential as biomarkers for cancer and as for therapeutic intervention using the metabolomics A. A.P. R. Y. A. S. J. D. S. A. S. profiles potential for in cancer PubMed Scopus Google Scholar). Moreover, used metabolomics to potential biomarkers with and treatment J. Y. M. Y. J. Y. to potential biomarkers for and Res. 2011; PubMed Scopus Google Scholar). There is also a in the alterations with EC by and based metabolomics H. for of cancer using PubMed Scopus Google Scholar, J. S. D. of esophageal 2011; PubMed Scopus Google Scholar). However, the and the of metabolic of ESCC by the metabolomics are from this study, a metabolomics based on in with multivariate statistical data was to determine global alterations in the metabolic profiles of healthy controls and ESCC patients before, during, and after alterations in to treatment were to potential and therapeutic biomarkers, a targeted metabolomics was carried out to validate the reliability of these potential biomarkers based on in selected reaction monitoring biomarker metabolites were by receiver operating characteristic (ROC) this on of potential therapeutic biomarkers in ESCC patients to with the of monitoring of treatment and A the is in and were from including l-carnitine, and were from and were from the for the Control of and of the were from the Cancer and of the of The cancer the The current patients were as patients the of with ESCC by for between 18 in performance status in in and and patients including the Cancer was to the Tumor Nodes Metastasis were in of cancer of the the and the The was by the and with the of with ESCC and post-treatment were after the of and of from ESCC patients with responses to treatment The of from and healthy in based validation ESCC and and of used in global metabolomics of this and of of of of of of in a were and in were for were and and of the and a of an for to Res. 2007; PubMed Scopus Google was by of A of plasma in study, and was J. F. for analysis based on J. 2011; Scopus Google Scholar). The was in of by of and for was and for RRLC-MS based validation plasma and were as separation was performed on a using an The was The was for were as and was for by to the and of the for A and were and and the was from healthy and ESCC patients were in in the analysis To the was analyzed the after plasma Mass were performed on a with ESI Data were in both and The were as and V. The was was used for both and Data and were performed using these was and the were in a by two on the most as a data were by with were of were in the Collision was to were the as RRLC-MS Data in were with and the were also with using of and which were by of and a of of and data were to specific metabolites to biological R. data for using and PubMed Scopus Google statistical was used in RRLC-MS data from plasma analysis were to using to with the and were using The were with the of data were as were three with and for data are in the the including in in and were further the for and were to data to multivariate statistical analysis to and in the Principal analysis was used to the To the of the the was as in analysis by using were selected to in and data in analysis an independent was used to determine biomarker from were between Partial between metabolites with on one were by a of was from a series of analysis F. T. using PubMed Scopus Google Scholar), and the and were The were as The with A and to in and A over The was to A in and for by to the and of the for A and were and and the was The was a with of the potential and therapeutic biomarkers were by The are as including including was used for data of metabolites was carried out methods Y. R. Y. J. J. J. with analysis of metabolic potential biomarkers for PubMed Scopus Google Scholar, J. J. P. P. R. for the and of biomarkers in a for the of for by the and PubMed Scopus Google and by the and using were used for further were to the of potential the potential biomarker was further for using the the receiver operating characteristic the of PubMed Scopus Google Scholar). was used to for this The of was and by A PubMed Scopus Google Scholar). the of metabolomics is a data that a the of an in and Res. 2007; PubMed Scopus Google Scholar). To from and RRLC-MS analysis be to multivariate data study, from were in in analysis Moreover, a was analyzed in with the to the of the of an for to Res. 2007; PubMed Scopus Google Scholar). Multivariate analysis results of the that the was that the data from the RRLC-MS were the profiles of plasma generated of than and for the of RRLC-MS analysis by ESI in and in results that the between groups by multivariate statistical analysis were more to be a of in than of from The from plasma in groups by are in useful metabolomics data analysis is as as the this study, the RRLC-MS analysis by ESI in both and data were was used to out and of and of between of used in by that were in the were using the S. R. S. metabolomics A for data and PubMed Scopus Google Scholar). The data were and and to the of and in the To global in both were and using However, the and groups were well in the is plasma are and the data analysis based on variation of metabolites A and To the separation of a further analysis using was in clear separation was four groups and for that metabolic perturbations were in the patients, on and treatment Moreover, the results also with the to the between and over with clear between in the indicated that treatment be in the of an separation was including and and separation between the and groups was A series of were to further the alterations that ESCC diagnosis and treatment further of potential biomarkers, an was using data from ESCC patients and healthy To potential therapeutic biomarkers, was using data from pre- and post-treatment ESCC patients, with the therapeutic and significantly altered in are more to be with therapeutic and potentially be used as biomarkers for therapeutic Comparison of between ESCC and To potential biomarker a of ESCC patients and healthy was selected to an independent and the the the was as a analysis for the of the between ESCC patients and for from both and clear of ESCC and groups and the classification of ESCC and groups resulted in one and two with a of of the in for of the of To further a of of three was with of the from the in with generated of and the data and one and two with validation of and results that from the data ability and were E. J. S. Multivariate and Data Analysis and Scholar). Moreover, to further the ability of the an independent was of in the which for the of in and the ESCC patients and healthy controls and validation the of the LC-MS based plasma metabolomics as a potential for ESCC. To potential biomarkers of the the and between the and the was of metabolites with a with a was S. E. J. T. J. of metabolomics data for of using PubMed Scopus Google Scholar). The the of on the with a of an on the of the E. J. S. Multivariate and Data Analysis and and and data were used to with an independent was used to validate the significance of the in between identified by these methods in with the were to from the such as and of these were also that from the metabolite. 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The the and of metabolomics with global and targeted to metabolic markers in ESCC and treatment with therapeutic effects. The global metabolomics the the data for of the from to Cell PubMed Scopus Google Scholar), the based targeted metabolomics are used to determine and relative and of a of and metabolites T. Y. M. for PubMed Scopus Google Scholar). to more biomarkers, it is to the global and targeted However, out is that and of were used in targeted to the of which to of the the of by of potential biomarkers with However, the of metabolites identified in this were and the biological significance of these potential biomarkers further the the of ESCC and treatment and the of biomarkers for diagnosis, treatment and in ESCC. with healthy controls, ESCC patients significantly plasma of l-carnitine, of (octanoylcarnitine, and were and are for the of long fatty the for and and that have the ability to fatty from the of the to the of and the for of patients with J. Clin. PubMed Scopus Google Scholar). this is to by J. and PubMed Scopus Google Scholar). the of be a of of in ESCC patients, and also the ability of of fatty have that to in and which are by the of Y. M. T. Y. Y. A. S. of cancer using and Res. PubMed Scopus Google Scholar, J. S. P. F. of cancer based on performance to with and PubMed Scopus Google Scholar, R. E. P. P. M. Cancer and on by PubMed Scopus Google Scholar, K. in in and plasma from patients with PubMed Scopus Google Scholar, in J. PubMed Scopus Google Scholar). four and have found as potential biomarkers in cancer patients J. S. P. F. of cancer based on performance to with and PubMed Scopus Google Scholar). also a have for an diagnosis of the biomarker panel be more in the of a series of fatty and were also in ESCC patients. fatty in a reaction by and in the to J. R. of fatty in and as and PubMed Scopus Google Scholar). The of the fatty and and and in ESCC patients are of and also be in a reaction by that the of be correlated with the of as the of in including and cancer Y. S. M. K. D. J. T. J. T. H. is in and to cell of by to PubMed Scopus Google Scholar, J. T. A. of to therapeutic 2005; PubMed Scopus Google Scholar, A. A. T. H. J. and of in J. Cancer. PubMed Scopus Google Scholar, J. S. S. of is closely to of cancer 2002; PubMed Scopus Google Scholar). the of is to be closely in the of of in the of cell Y. P. H. S. M. Y. potential biomarkers for Clin. 2007; PubMed Scopus Google Scholar, The of in Cancer. PubMed Scopus Google Scholar). have as Y. P. H. S. M. Y. potential biomarkers for Clin. 2007; PubMed Scopus Google Scholar). the of may an in this in cancer patients. also a in of the of in the in ESCC patients. metabolic is to the of from and sterol M. H. T. K. J. by PubMed Scopus Google Scholar). as the of was found to be in plasma of ESCC patients The studies carcinoma also was in of patients variation in J. Y. M.M. Y. P. and potential biomarkers of 2011; PubMed Scopus Google Scholar). The of and also a of and is a of of and a in A of studies have that is with an increase in and and D. K. J. of and as metabolic biomarkers of cancer using of PubMed Scopus Google Scholar, S. and of PubMed Scopus Google Scholar). is an in the The of in in 1980; Scopus Google and is also a in metabolic including the of fatty R. Control of and between fatty and the in PubMed Google Scholar). of these metabolites were also in studies on cancer metabolomics Y. M. T. Y. Y. A. S. of cancer using and Res. PubMed Scopus Google Scholar, J. S. P. F. of cancer based on performance to with and PubMed Scopus Google Scholar, R. E. P. P. M. Cancer and on by PubMed Scopus Google and K. in in and plasma from patients with PubMed Scopus Google Scholar), of metabolites in were were the was in cancer Y. M. T. Y. Y. A. S. of cancer using and Res. PubMed Scopus Google and cancer J. S. P. F. of cancer based on performance to with and PubMed Scopus Google Scholar), the was in cancer J. S. P. F. of cancer based on performance to with and PubMed Scopus Google Scholar). However, the in patients with was were significantly in plasma K. in in and plasma from patients with PubMed Scopus Google Scholar). the of the in the of the biomarker be of for a analysis on a generated to the of the biomarker The in of the and with the and was with a and to and further more validation be and more including cancer patients, patients, and healthy controls, be in the to further the and of this biomarker The of three metabolites and decanoylcarnitine) before, during, and after treatment are of in and in ESCC patients as a of A studies the of the by R. E. P. P. M. Cancer and on by PubMed Scopus Google Scholar, S. E. P. S. of in patients with PubMed Scopus Google Scholar, Y. T. Y. S. S. Y. H. of on in by 2011; PubMed Scopus Google Scholar). However, the a of these three metabolites between and over the treatment more over that these three metabolites be useful in diagnosis, also in monitoring treatment and the revealed that metabolomics global with targeted is to biomarkers applicable to both and therapeutic The results the global metabolic alterations of healthy controls and ESCC patients before, and after treatment by RRLC-MS based metabolomics which were potentially both in diagnosis and monitoring the therapeutic and in and in ESCC. the analysis of be to further validate the of biomarkers in this with

An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (+/-) APCI-MS accounted for 45 and 42% of the overall; meanwhile (+/-) APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods.