Cystic Fluid Total Proteins, Low-Density Lipoprotein Cholesterol, Lipid Metabolites, and Lymphocytes: Worrisome Biomarkers for Intraductal Papillary Mucinous Neoplasms

Cancers, Journal Year: 2025, Volume and Issue: 17(4), P. 643 - 643

Published: Feb. 14, 2025

Objectives: Pancreatic cystic neoplasms (PCNs), particularly intraductal papillary mucinous (IPMNs), present a challenge for their potential malignancy. Despite promising biomarkers like CEA, amylase, and glucose, our study investigates whether metabolic indices in blood fluids (CFs), addition to lymphocyte subsets hematopoietic stem/progenitor cells (HSPCs), can effectively differentiate between high- low-risk PCNs. Materials Methods: A total of 26 patients (11 males, mean age 69.5 ± 9 years) undergoing Endoscopic Ultrasound-guided Fine Needle Aspiration were consecutively enrolled. Analyses included blood, serum, CF, assessing cholesterol (total, HDL, LDL), proteins. Flow cytometry examined immunophenotyping peripheral fluids. Mass spectrometry was used the metabolomic analysis CF. Sensitivity, specificity, ROC analyses evaluated discriminatory power. Results: 25 out had IPMN. Patients categorized as low or high risk based on multidisciplinary evaluation clinical, radiological, endoscopic data. High-risk showed lower CF proteins LDL (p = 0.005 p 0.031), with marked reduction lymphocytes 0.005). HSCPs absent In high-risk increased non-MHC-restricted cytotoxic T 0.019). The revealed significantly reduced middle long-chain acyl carnitines (AcCa) tryptophan metabolites patients. curves indicated comparable discriminant abilities (AUC 0.868), 0.859), 0.795). highest performance achieved by AcCa 14:2 16:0 (AUC: 0.9221 0.8857, respectively). Conclusions: levels cholesterol, together counts are easy translational that may support stratification PCNs IPMN might be endorsed analysis. Further studies required clinical integration.

Language: Английский

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 3, 2024

Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions identifying tumor-derived biomarkers.

Language: Английский

Transcriptomic and metabolomic analyses reveal the spatial role of carnitine metabolism in the progression of hepatitis B virus cirrhosis to hepatocellular carcinoma

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 13, 2024

Introduction Liver cirrhosis (LC) and hepatocellular carcinoma (HCC) resulting from chronic hepatitis B virus (HBV) infection are major health concerns. Identifying critical biomarkers molecular targets is needed for early diagnosis, prognosis, therapy of these diseases. Methods In this study, we explored the gene expression metabolism in liver tissues LC, HCC, healthy controls, to analyse identify potential disease progression. Mass spectrometry imaging was used evaluate spatial distribution key metabolites. Results discussion The results revealed significant changes metabolic pathways along with upregulated genes were associated extracellular matrix remodeling cancer pathways, including LAMC1-3, COL9A2, COL1A1, MYL9, MYH11, KAT2A. downregulated linked immune response fatty acid metabolism. Metabolomic analysis showed lipid choline Consistent specific metabolites correlated clinical data. Notably, such as L-acetylcarnitine, histamine, 4-trimethylammoniobutanoic demonstrated high accuracy (AUC > 0.85) distinguishing between healthy, HCC groups. This study identifies metabolite HBV related LC highlighting involved Biomarkers like L-acetylcarnitine KAT2A show promise diagnosis potentially improving outcomes patients.

Language: Английский