BIOINFORMATIC ANALYSIS IDENTIFIES POTENTIALLY KEY DIFFERENTIALLY EXPRESSED GENES AND PATHWAYS IN ORBITAL ADIPOSE TISSUES OF PATIENTS WITH THYROID EYE DISEASE

Hub genes and key pathways of Graves' disease: bioinformatics analysis and validation

OBJECTIVE

This study aims to identify hub genes associated with the onset and progression of Graves' disease (GD) with the goal of developing novel biomarkers to enhance diagnosis and improve patient outcomes.

METHODS

mRNA profiles from thyroid tissue samples (24 GD vs. 24 normal controls) were obtained from GEO (GSE9340), ArrayExpress (E-MEXP-2612), and GTEx (Thyroid dataset). After batch correction via SVA algorithm, 366 differentially expressed genes (DEGs) were identified using limma. Functional enrichment, protein-protein interaction networks, and immune microenvironment analysis were performed. Hub genes were validated in clinical thyroid specimens (3 GD vs. 3 controls) using RT-qPCR.

RESULTS

A total of 366 DEGs were identified in the diseased and normal groups. Among these, eight hub genes (TYROBP, CSF1R, CD163, ITGAM, CD86, FCGR3B, ITGB2, and IL10RA) showed strong correlations with immune cell content. These genes were predominantly enriched in pathways related to amino acid metabolism, viral protein interactions with cytokines and cytokine receptors, phagosome, chemokine signaling, programmed cell death, NF-κB, and other pathways. Additionally, these hub genes were linked to 39 regulatory factors. mRNA levels of these hub genes were validated in clinical samples through RT-qPCR. It is noteworthy that eight genes were found to be upregulated in GD samples.

CONCLUSION

The study highlights the potential impact of ITGB 2, TYROBP, CSF1R, CD163, ITGAM, CD86, FCGR3B, and IL10RA on the development and progression of GD, supporting their role as potential biomarkers.

Roles of four targets in the pathogenesis of graves' orbitopathy

Graves' orbitopathy (GO) is an autoimmune disease that involves complex immune systems. The mainstays of clinical management for this disease are surgery, targeted drugs therapy, and no-targeted drugs drug therapy. targeted drugs can improve therapeutic efficacy and enhance the quality of life for GO patients. However, as a second-line treatment for GO, targeted drugs such as tocilizumab and rituximab have very limited therapeutic effects and may be accompanied by side effects. The introduction of Teprotumumab, which targets IGF-IR, has made significant progress in the clinical management of GO. The pathophysiology of GO still remains uncertain as it involves a variety of immune cells and fibroblast interactions as well as immune responses to relevant disease targets of action. Therfore, learning more about immune response feedback pathways and potential targets of action will assist in the treatment of GO. In this discussion, we explore the pathogenesis of GO and relevant work, and highlight four potential targets for GO: Interleukin-23 receptor (IL-23 R), Leptin receptor (LepR), Orbital fibroblast activating factors, and Plasminogen activator inhibitor-1 (PAI-1). A deeper understanding of the pathogenesis of GO and the role of potential target signaling pathways is crucial for effective treatment of this disease.

Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches

Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated with the development of CRC are not fully demonstrated. Objectives: This study aimed to identify differentially expressed miRNAs (DEMs), key genes, and their regulators associated with the pathogenesis of CRC. The signaling pathways and biological processes (BPs) that were significantly affected in CRC were also indicated. Moreover, two non-invasive models were constructed for CRC diagnosis. Methods: The miRNA dataset GSE59856 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify DEMs in CRC patients compared with healthy controls (HCs). A protein-protein interaction (PPI) network was built and analyzed. Significant clusters in the PPI networks were identified, and the BPs and pathways associated with these clusters were studied. The hub genes in the PPI network, as well as their regulators were identified. Results: A total of 569 DEMs were demonstrated with the criteria of P value <0.001. A total of 110 essential genes and 30 modules were identified in the PPI network. Functional analysis revealed that 1005 BPs, 9 molecular functions (MFs), 14 cellular components (CCs), and 887 pathways were significantly affected in CRC. A total of 22 transcription factors (TFs) were demonstrated as the regulators of the hubs. Conclusion: Our results may provide new insight into the pathogenesis of CRC and advance the diagnostic and therapeutic methods of the disease. However, confirmation is required in the future.