Identification of key interactive genes and their potential biological functions in type 2 diabetes and Sjögren's syndrome.

This study aimed to identify crosstalk genes and explore their potential roles in type 2 diabetes (T2D) and Sjögren's syndrome (SS) using bioinformatic analysis. We analyzed multiple publicly available gene expression datasets and screened 16 crosstalk genes. Consequently, genes that may play significant roles in disease processes were identified. Thereafter, we used gene set variation analysis to assess the differences in gene sets among various samples. LASSO regression analysis was performed to determine the optimal diagnostic genes, and predictive models for T2D and SS were constructed. The classification accuracy of these models was evaluated using receiver operating characteristic curves. Among 16 identified crosstalk genes, 11 showed significant differences in expression. These genes were significantly enriched in biological processes. The predictive model generated from ALDH6A1 and IL11RA demonstrated good classification accuracy for T2D and SS samples. Immune infiltration analysis revealed significant differences in specific immune cell types between the disease and control groups, demonstrating a significant correlation with the identified hub genes, highlighting their potential involvement in T2D and SS pathophysiology. This study revealed the crucial role of specific immune cells in T2D and SS pathology, providing new insights into both conditions and the potential targets for future immunotherapy design.