Fecal Metabolic Profiling of Breast Cancer Patients during Neoadjuvant Chemotherapy Reveals Potential Biomarkers.
Oumaima ZidiNessrine SouaiHenda RaiesFarhat Ben AyedAmel MezliniSonia MezriouiFabrice TranchidaJean Marc SabatierAmor MosbahAmeur CherifLaetitia ShintuSoumaya KouidhiPublished in: Molecules (Basel, Switzerland) (2021)
Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.
Keyphrases
- neoadjuvant chemotherapy
- end stage renal disease
- ejection fraction
- newly diagnosed
- peritoneal dialysis
- locally advanced
- young adults
- prognostic factors
- mass spectrometry
- squamous cell carcinoma
- transcription factor
- radiation therapy
- machine learning
- smoking cessation
- electronic health record
- combination therapy
- fatty acid
- patient reported outcomes
- squamous cell
- artificial intelligence