Journal Article Epublish
PubMed: 40649706 DOI: 10.3390/ijms26135926
A Pilot Metabolomic Study for Diagnosing Aspergillus Infection in Immunocompromised Pediatric Cancer Patients.
Int J Mol Sci 26(13)
Authors: Taghreed Khaled Abdelmoneim, Asmaa Ramzy, Mostafa Ahmed Zaki, Ahmed Karam, Ahmed Hesham, Aya Osama, Nabila Sabar, Maha Mokhtar, Nada A Youssef, Eman A Ahmed, Lobna Shalaby, Asmaa Salama, Ahmed Kamel, Mervat Elenany, Sameh Magdeldin
Affiliations
- Proteomics and Metabolomics Research Program, Basic Research Department, Children's Cancer Hospital Egypt, Cairo 57357, Egypt.
- Department of Pharmacology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt.
- Infectious Diseases Department, Children's Cancer Hospital Egypt, Cairo 57357, Egypt.
- Pediatric Oncology Department, National Cancer Institute, Cairo University, Giza 12613, Egypt.
- Pathology Department, Children's Cancer Hospital Egypt, Cairo 57357, Egypt.
- Pathology Department, National Cancer Institute, Cairo University, Giza 12613, Egypt.
- Microbiology Section, Clinical Pathology Department, Children's Cancer Hospital Egypt, Cairo 57357, Egypt.
- Microbiology Section, Clinical Pathology Department, Children's Cancer Hospital Egypt, Cairo 57357, Egypt. Clinical Pathology, Faculty of Medicine, Cairo University, Giza 12613, Egypt.
- Physiology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt.
Abstract
Fungal infection caused by invasive Aspergillus is a life-threatening complication in immunocompromised pediatric cancer patients. However, the early diagnosis of invasive infection remains a clinical challenge due to the lack of specific, non-invasive biomarkers. The current study investigates plasma metabolomic profiling integrated with an AI-derived fungal secondary metabolite database to identify potential biomarkers for rapid, non-invasive detection of Aspergillus infection. Plasma samples from thirteen pediatric oncology patients were analyzed using untargeted metabolomics based on UHPLC-MS/MS. Based on galactomannan assay results, three patients were classified as Aspergillus-Infected (AIC) and ten as non-infected controls (NPCs). An in-house custom database for secondary metabolites of fungi was incorporated to enhance metabolite annotation. Eight metabolites were found to be candidate biomarkers based on statistical significance, fold change, and biological relevance. In the AIC cohort, aflatoxin B1, aspergillimide, fumifungin, and uridine were found to be significantly elevated while citric acid presented a decrease. Multivariate analysis utilizing PCA and PLSDA showed distinct group separation. Moreover, sample size estimation indicates that a minimum of 25 participants would be needed in future studies for appropriate statistical power.
MeSH
Pilots, Neoplasms, Biomarkers, Metabolomics, Aspergillus, Mycoses, Fungi, Artificial Intelligence, Aspergillosis, Infections