Somayeh Ramesh, Akram Alizadeh Moghdam, Ali Reza Safari, Marzieh Feizi,
Volume 18, Issue 2 (2-2019)
Abstract
Background: Diabetes mellitus is one of the most common chronic diseases and the patient's quality of life plays an important role in controlling the disease. The purpose of this study was to investigate the mediating role of quality of life in the relationship between depression, stress and anxiety, with severity of diabetes.
Methods: 108 patients with type 2 diabetes (57 women, 51 males) participated in this study. The participants completed the 21st-DASS Questionnaire, a quality of life questionnaire (SF-36), and a demographic questionnaire.
Results: The results of the study showed that the severity of the disease was negatively correlated with quality of life and positively correlated with anxiety, depression and stress (P <0.01). The results of path analysis also indicated the mediating role of quality of life in the relationship between depression and anxiety and the severity of type 2 diabetes.
Conclusion: Based on the results, it is necessary to consider psychological interventions in order to reduce depression and anxiety and improve the quality of life of patients in the field of diabetes management.
Nahid Safari-Alighiarloo, Seyyed Mohammad Tabatabaei, Nasibeh Khayer, Nahid Hashemi Madani, Mohammad E. Khamseh,
Volume 25, Issue 6 (1-2026)
Abstract
Background: Nonfunctioning pituitary adenomas (NFPAs) are among the most prevalent subtypes of pituitary adenomas, presenting no clinical hormone elevation. The lack of definitive biomarkers for prognosis and treatment, combined with a significant risk of recurrence, poses substantial challenges to management. This study aims to identify key genes and biological pathways involved in NFPAs tumorigenesis using a systems biology approach.
Methods: The dataset with accession number GSE26966 was analyzed to identify differentially expressed genes (DEGs) in NFPAs. Interactions between DEGs at the protein level were constructed using protein-protein interaction (PPI) data collected from the IntAct database. Cytoscape software, igraph, and MCL packages were used to construct the PPI network, analyze its topology, and cluster it.
Results: 1,135 differential genes were identified between NFPAs and normal pituitary samples based on |log2FC|>2 and FDR < 0.05. Of these, 323 were up-regulated and 812 were down-regulated. The constructed PPI network consisted of 6,960 nodes and 15,691 edges. According to network clustering, cell cycle regulation, chromatin organization and assembly regulation, transcription regulation, and actin cytoskeleton regulation were the most significant pathways. Using topological analysis, CDKN1A, BHLHE40, FHL2, H1-2, H2BC21, and FGFR3 were identified as central hub nodes in the PPI network. These genes were also involved in the biological pathways mentioned above.
Conclusion: This study demonstrated that a systems biology approach, integrating gene transcriptome data with protein interaction data, can effectively identify pathways and biomarkers in NFPAs tumorigenesis.
