Identification of highly sensitive biomarkers that can aid the early detection of pancreatic cancer using GC/MS/MS-based targeted metabolomics

Hirata, Yuichi; Kobayashi, Takashi; Nishiumi, Shin; Yamanaka, Kodai; Nakagawa, Takashi; Fujigaki, Seiji; Iemoto, Takao; Kobayashi, Makoto; Okusaka, Takuji; Nakamori, Shoji; Shimahara, Masashi; Ueno, Takaaki; Tsuchida, Akihiko; Sata, Naohiro; Ioka, Tatsuya; Yasunami, Yohichi; Kosuge, Tomoo; Kaneda, Takashi; Kato, Takao; Yagihara, Kazuhiro; Fujita, Shigeyuki; Yamada, Tesshi; Honda, Kazufumi; Azuma, Takeshi; Yoshida, Masaru

doi:10.1016/j.cca.2017.02.011

Cited by 40 publications

(37 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These metabolic changes were consistent with our previous report for pancreatic cancer patients . In addition, we verified the usefulness of these metabolites for detection of early‐stage pancreatic cancer patients (stages I and II), and these metabolites had a high sensitivity for early‐stage pancreatic cancer. Therefore, these previous results support the high reliability of our present findings.…”

Section: Discussionsupporting

confidence: 92%

“…These facts indicate that tyrosine metabolism may be related to pancreatic cancer. In subjects who were diagnosed in the first 0‐6 years, asparagine, uric acid, and tyrosine showed a similar tendency (Table ), and these results support our previous report …”

Section: Discussionsupporting

confidence: 90%

“…We focused on 12 metabolites that have previously been reported to be biomarker candidates; ie, threonine, methionine, arabinose, asparagine, xylitol, glutamine, 1,5‐AG, lysine, histidine, tyrosine, inositol and uric acid. Stable isotope products for each metabolite; ie, l ‐threonine [13C4, 97%‐99%], l ‐methionine [13C5, 99%], d ‐arabinose [U‐13C5, 99%], l ‐asparagine:H 2 O [13C4, 99%], d ‐xylitol [U‐13C5, 99%], l ‐glutamine [13C5, 99%], 1,5‐AG [U‐13C6, 98%+], l ‐lysine:2HCl [13C6, 99%], l ‐histidine:HCl:H 2 O [13C6, 97%‐99%], l ‐tyrosine [ring‐13C6, 99%], myo‐inositol [1,2,3,4,5,6‐D6, 98%], and uric acid [1,3‐15N2, 98%], were obtained from Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…These studies showed the potential utility of metabolomics in the detection of metabolic changes as a result of premalignant pancreatic changes and evaluation of environmental factors. We reported that the levels of some metabolites differ significantly between pancreatic cancer patients and healthy volunteers, and the serum levels of valine, 2‐aminoethanol, threonine, creatinine, asparagine, xylitol, 1,5‐anhydro‐ d ‐glucitol (1,5‐AG), lysine, histidine, tyrosine and inositol in pancreatic cancer patients were significantly lower than in healthy volunteers . Changes of these metabolites may reflect metabolic changes as a result of pancreatic cancer or environmental factors or both.…”

Section: Introductionmentioning

confidence: 98%

“…We reported that the levels of some metabolites differ significantly between pancreatic cancer patients and healthy volunteers, and the serum levels of valine, 2-aminoethanol, threonine, creatinine, asparagine, xylitol, 1,5-anhydro-D-glucitol (1,5-AG), lysine, histidine, tyrosine and inositol in pancreatic cancer patients were significantly lower than in healthy volunteers. 13,14 Changes of these metabolites may reflect metabolic changes as a result of pancreatic cancer or environmental factors or both. If some of these metabolite changes start before the occurrence of pancreatic cancer, we can apply these metabolites to predictive markers to identify the high-risk group of pancreatic cancer.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Metabolome analysis for pancreatic cancer risk in nested case‐control study: Japan Public Health Center‐based prospective Study

et al. 2018

Self Cite

View full text Add to dashboard Cite

Discovery of a high‐risk group for pancreatic cancer is important for prevention of pancreatic cancer. The present study was conducted as a nested case‐control study including 170 pancreatic cancer cases and 340 matched controls of our population‐based cohort study involving 30 239 subjects who answered a baseline questionnaire and supplied blood samples. Twelve targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry. Odds ratios (OR) and their corresponding 95% confidence intervals (CI) were calculated using conditional logistic regression models. Statistically significant P‐value was defined as P < .05. Increasing 1,5‐anhydro‐d‐glucitol (1,5‐AG) levels were associated with a decreasing trend in pancreatic cancer risk (OR of quartile 4 [Q4], 0.50; 95% CI, 0.27‐0.93; P = .02). Increasing methionine levels were also associated with an increasing trend of pancreatic cancer risk (OR of Q4, 1.79; 95% CI, 0.94‐3.40: P = .03). Additional adjustment for potential confounders attenuated the observed associations of 1,5‐AG and methionine (P for trend = .06 and .07, respectively). Comparing subjects diagnosed in the first 0‐6 years, higher levels of 1,5‐AG, asparagine, tyrosine and uric acid showed a decreasing trend for pancreatic cancer risk (P for trend = .04, .04, .04 and .02, respectively), even after adjustment for potential confounders. We found that the 12 target metabolites were not associated with pancreatic cancer risk. However, metabolic changes in the subjects diagnosed in the first 0‐6 years showed a similar tendency to our previous reports. These results might suggest that these metabolites are useful for early detection but not for prediction of pancreatic cancer.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Metabolome analysis for pancreatic cancer risk in nested case‐control study: Japan Public Health Center‐based prospective Study

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

Zhao

Ren

et al. 2022

Cancer Medicine

View full text Add to dashboard Cite

Background Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis. Methods We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis. Results Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34–3.53]). The three markers showed area under the receiver‐operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19‐9 (CA19‐9) was added to the model. Conclusion The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19‐9.

show abstract

Advances in mass spectrometry imaging for spatial cancer metabolomics

Fernández

2022

Mass Spectrometry Reviews

View full text Add to dashboard Cite

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in‐depth understanding of cancer‐associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three‐dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI‐based cancer studies. The adoption of MSI in clinical research and for single‐cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

show abstract

Identification of highly sensitive biomarkers that can aid the early detection of pancreatic cancer using GC/MS/MS-based targeted metabolomics

Abstract: Quantitative analysis confirmed that metabolomics-based diagnostic methods are useful for detecting PC early.

Cited by 40 publications

References 29 publications

Metabolome analysis for pancreatic cancer risk in nested case‐control study: Japan Public Health Center‐based prospective Study

Metabolome analysis for pancreatic cancer risk in nested case‐control study: Japan Public Health Center‐based prospective Study

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

Advances in mass spectrometry imaging for spatial cancer metabolomics

Contact Info

Product

Resources

About