Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

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Abstract

Background

Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).

Methods

A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.

Results

We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816–0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716–0.805).

Conclusions

These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-023-11744-y.

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Data, information, knowledge and principle: back to metabolism in KEGG

Minoru Kanehisa, Susumu Goto, Yoko Sato … (2013)

In the hierarchy of data, information and knowledge, computational methods play a major role in the initial processing of data to extract information, but they alone become less effective to compile knowledge from information. The Kyoto Encyclopedia of Genes and Genomes (KEGG) resource (http://www.kegg.jp/ or http://www.genome.jp/kegg/) has been developed as a reference knowledge base to assist this latter process. In particular, the KEGG pathway maps are widely used for biological interpretation of genome sequences and other high-throughput data. The link from genomes to pathways is made through the KEGG Orthology system, a collection of manually defined ortholog groups identified by K numbers. To better automate this interpretation process the KEGG modules defined by Boolean expressions of K numbers have been expanded and improved. Once genes in a genome are annotated with K numbers, the KEGG modules can be computationally evaluated revealing metabolic capacities and other phenotypic features. The reaction modules, which represent chemical units of reactions, have been used to analyze design principles of metabolic networks and also to improve the definition of K numbers and associated annotations. For translational bioinformatics, the KEGG MEDICUS resource has been developed by integrating drug labels (package inserts) used in society.

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From Krebs to clinic: glutamine metabolism to cancer therapy.

Brian J. Altman, Zachary E Stine, Chi Dang (2016)

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Histological precursors of oesophageal squamous cell carcinoma: results from a 13 year prospective follow up study in a high risk population.

Y Qiao, Guirong Wang, Xiao Sun … (2005)

Oesophageal squamous cell carcinoma (OSCC) has a very poor prognosis, which is largely due to late diagnosis. Successful early detection strategies will require identification of clinically relevant precursor lesions that can be targets for screening and treatment. To identify the clinically relevant histological precursors of OSCC. A cohort of 682 endoscoped patients from a high risk rural population in Linxian, China. Subjects were endoscoped and biopsied at baseline and followed for 13.5 years. We estimated the relative risk of developing OSCC for each of the initial histological diagnoses using Cox proportional hazards regression models. A total of 114 (16.7%) patients developed OSCC during the follow up period. After adjusting for potential confounding factors, relative risks (95% confidence intervals) for incidence of this tumour, by initial histological diagnosis, were: normal 1.0 (reference), oesophagitis 0.8 (0.2-3.2), basal cell hyperplasia 1.9 (0.8-4.5), mild dysplasia 2.9 (1.6-5.2), moderate dysplasia 9.8 (5.3-18.3), severe dysplasia 28.3 (15.3-52.3), and carcinoma in situ 34.4 (16.6-71.4). In this study, squamous dysplasia and carcinoma in situ were the only histological lesions associated with a significantly increased risk of developing OSCC within 13.5 years after endoscopy. There was no evidence that oesophagitis predisposed to this tumour. Increasing grades of dysplasia were strongly associated with increasing risk, indicating that the histological grading was clinically meaningful. The follow up experience of severe dysplasia and carcinoma in situ was equivalent, suggesting that this distinction is not clinically relevant. Documenting these precursor lesions of OSCC should assist in the development of effective prevention, early detection, and treatment strategies for this disease.

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Author and article information

Contributors

Cheng Wang: chengwang@sdu.edu.cn

Jialin Wang: wangjialin6681@gmail.com

Tao Zhang: taozhang@sdu.edu.cn

Journal

Journal ID (nlm-ta): BMC Cancer

Journal ID (iso-abbrev): BMC Cancer

Title: BMC Cancer

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2407

Publication date (Electronic): 15 December 2023

Publication date PMC-release: 15 December 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 1238

Affiliations

[1 ]Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, ( https://ror.org/0207yh398) Jinan, 250012 Shandong China

[2 ]Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, ( https://ror.org/0207yh398) Jinan, 250012 China

[3 ]GRID grid.440144.1, ISNI 0000 0004 1803 8437, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, ; 440 Jiyan Road, Jinan, 250117 China

Article

Publisher ID: 11744

DOI: 10.1186/s12885-023-11744-y

PMC ID: 10724912

PubMed ID: 38102546

SO-VID: 4a10ef13-9a04-4847-a531-25c1ba686668

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 3 October 2023

Date accepted : 12 December 2023

Funding

Funded by: National Natural Science Foundation of China

Award ID: 82222064

Funded by: National Key Research and Development Program

Award ID: 2022YFC2010100

Funded by: Shandong University Distinguished Young Scholars

Custom metadata

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: gastroesophageal cancer,esophageal squamous cell carcinoma,cancer progression,metabolomics,biomarker.

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: gastroesophageal cancer, esophageal squamous cell carcinoma, cancer progression, metabolomics, biomarker.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Journal of Circulating Biomarkers

Most cited references 30

Data, information, knowledge and principle: back to metabolism in KEGG

From Krebs to clinic: glutamine metabolism to cancer therapy.

Histological precursors of oesophageal squamous cell carcinoma: results from a 13 year prospective follow up study in a high risk population.

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