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Metabolites
Special Issues
Mass Spectrometry-Based Metabolomics for Advancing Personalized Medicine
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Mass Spectrometry-Based Metabolomics for Advancing Personalized Medicine

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 924

Special Issue Editors

Prof. Dr. Cristina Adela Iuga

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Guest Editor
Department of Personalized Medicine and Rare Diseases, Institute of Biomedical Research—MedFuture, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
Interests: integrated omics (proteomics and metabolomics); personalized medicine; separation science; mass spectrometry
Special Issues, Collections and Topics in MDPI journals
Dr. Radu-Cristian Moldovan

E-Mail Website
Guest Editor
Department of Personalized Medicine and Rare Diseases, Institute of Biomedical Research—MedFuture, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
Interests: metabolomics; separation science; mass spectrometry; chiral analysis

Special Issue Information

Dear Colleagues,

Along with technological advancements in the bioanalytical field, modern metabolomics techniques are powerful tools capable of offering comprehensive snapshots of metabolic changes occurring in the human body, providing critical insights into physiological processes, disease pathogenesis, and potential biomarkers for diagnosis and treatment. Translating these observations to clinical practice often involves deciphering intricate disease mechanisms. Together with other “omics” technologies, metabolomics plays a pivotal role in personalized medicine, reflecting individual responses to diseases or treatments.

This Special Issue of Metabolites is focused on mass-spectrometry-based metabolomics in the context of personalized medicine, and investigators are invited to present their relevant research results. This Special Issue aims to focus on relevant advancements using mass spectrometry tools for metabolomic research, with a focus on personalized medicine. These topics may include biomarker discovery and validation for diagnosis and disease stratification, together with methodological advancements and integration with other “omics” technologies to better understand metabolic pathway alterations. 

Prof. Dr. Cristina Adela Iuga
Dr. Radu-Cristian Moldovan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mass spectrometry-based metabolomics
  • integrated-omics
  • personalized medicine
  • biomarker discovery

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Published Papers (1 paper)

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Research

20 pages, 6441 KiB  
Article
Tissue-Based Metabolomic Profiling of Endometrial Cancer and Hyperplasia
by Khalid Akkour, Afshan Masood, Maha Al Mogren, Reem H. AlMalki, Assim A. Alfadda, Salini Scaria Joy, Ali Bassi, Hani Alhalal, Maria Arafah, Othman Mahmoud Othman, Hadeel Mohammad Awwad, Anas M. Abdel Rahman and Hicham Benabdelkamel
Metabolites 2025, 15(7), 458; https://doi.org/10.3390/metabo15070458 - 5 Jul 2025
Viewed by 677
Abstract
Background: Endometrial cancer (EC) is the sixth most common cancer among women globally, with an estimated 420,000 new cases diagnosed annually. Methods: This study comprised patients with endometrial cancer (EC) (n = 17), hyperplasia (HY) (n = 17), and controls (CO) [...] Read more.
Background: Endometrial cancer (EC) is the sixth most common cancer among women globally, with an estimated 420,000 new cases diagnosed annually. Methods: This study comprised patients with endometrial cancer (EC) (n = 17), hyperplasia (HY) (n = 17), and controls (CO) (n = 20). Tissue was collected from the endometrium of all 54 patients, including patients with HY, EC, and CO, who underwent total hysterectomy. EC and HY diagnoses were confirmed based on histological examination. Untargeted metabolomics profiling was conducted using LC-HRMS. The partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used for univariate and multivariate statistical analysis. The fitness of the model (R2Y) and predictive ability (Q2) were used to create OPLS-DA models. ROC analysis was carried out, followed by network analysis using Ingenuity Pathway Analysis. Results: The top metabolites that can discriminate EC and HY from CO were identified. This revealed a decrease in the levels of the lipid species, specifically phosphatidic acid (PA) (PA (14:1/14:0), PA(10:0/17:0), PA(18:1-O(12,13)/12:0)), PG(a-13:0/a-13:0), ganglioside GA1 (d18:1/18:1), PS(14:1/14:0), TG(20:0/18:4/14:1), and CDP-DG(PGF2alpha/18:2), while the levels of 3-Dehydro-L-gulonate, Uridine diphosphate-N-acetylglucosamine, ganglioside GT2 (d18:1/14:0), gamma-glutamyl glutamic acid and oxidized glutathione were increased in cases of EC and HY as compared to CO. Bioinformatics analysis, specifically using Ingenuity Pathway Analysis (IPA), revealed distinct pathway enrichments for EC and HY. For EC, the most highly scored pathways were associated with cell-to-cell signaling and interaction, skeletal and muscular system development and function, and small-molecule biochemistry. In contrast, HY cases showed the highest scoring pathways related to inflammatory disease, inflammatory response, and organismal injury and abnormalities. Conclusions: Developing sensitive biomarkers could improve diagnosis and guide treatment decisions, particularly in identifying which patients with HY may safely avoid hysterectomy and be managed with hormonal therapy. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics for Advancing Personalized Medicine)
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