Integrative proteomic analysis reveals the potential diagnostic marker and drug target for the Type-2 diabetes mellitus.

Abstract

Objective: The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.

Methods: Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls. Differential expression analysis, functional enrichment analysis, receiver operating characteristic (ROC) analysis, and classification error matrix analysis were employed.

Results: Comparative proteomic analysis identified the differential expressed proteins (DEPs) and changes in biological pathways associated with T2DM. Further combined analysis refined a group of protein panel (including CA1, S100A6, and DDT), which were significantly increased in T2DM in both two cohorts. ROC analysis revealed the area under curve (AUC) values of 0.94 for CA1, 0.87 for S100A6, and 0.97 for DDT; the combined model achieved an AUC reaching 1. Classification error matrix analysis demonstrated the combined model could reach an accuracy of 1 and 0.875 in the 60% training set and 40% testing set.

Conclusions: This study incorporates different cohorts of T2DM, and refines the potential markers for T2DM with high accuracy, offering more reliable markers for clinical translation.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01562-3.