Identification and investigation of protein-related molecules in patients with hyperlipidemia using label-free combined with bioinformatics analysis

Abstract

This study aimed to identify proteins associated with high-fat diet patients and investigate their relationship with this dietary pattern. Five hyperlipidemia female patients and five normal individuals were included as the experiment and control groups, respectively. Blood samples were collected from both groups, and bioinformatics tools were employed for gene ontology annotation, KEGG pathway annotation, GO enrichment analysis, pathway enrichment analysis, and protein clustering to pinpoint genes, proteins, and pathways relevant to high-fat diet patients. Mass spectrometry analysis was subsequently used to confirm these proteins. The results indicated that bioinformatics analysis identified several proteins (P09871, P01019, P48740, P02654, P02649) potentially involved in the high-fat diet process by regulating downstream pathways. Label-free analysis revealed 3915 peptides in both groups, with 16 protein expression levels up-regulated in the experiment group, 13 of which showed significant differences. In contrast, 12 protein expression levels were down-regulated in the experiment group, with two showing significant differences. Notably, the proteins highlighted by bioinformatics analysis aligned with those identified through mass spectrometry. In conclusion, label-free analysis combined with bioinformatics can effectively identify proteins linked to high-fat diet patients. This research provides a fresh perspective on addressing high-fat diet-related issues using this approach.