Whole-Exome Screening and Analysis of Signaling Pathways in Multiple Endocrine Neoplasia Type 1 Patients with Different Outcomes: Insights into Cellular Mechanisms and Possible Functional Implications.
Anna Elżbieta SkalniakMałgorzata Trofimiuk-MüldnerMarcin SurmiakJustyna Totoń-ŻurańskaAgata Jabrocka-HybelAlicja Hubalewska-DydejczykPublished in: International journal of molecular sciences (2024)
Multiple endocrine neoplasia type 1 (MEN1) is a syndrome characterized by tumors in multiple organs. Although being a dominantly inherited monogenic disease, disease phenotypes are unpredictable and differ even among members of the same family. There is growing evidence for the role of modifier genes in the alteration of the course of this disease. However, genome-wide screening data are still lacking. In our study, we addressed the different outcomes of the disease, focusing on pituitary and adrenocortical tumors. By means of exome sequencing we identified the affected signaling pathways that segregated with those symptoms. Most significantly, we identified damaging alterations in numerous structural genes responsible for cell adhesion and migration. Additionally, in the case of pituitary tumors, genes related to neuronal function, survival, and morphogenesis were repeatedly identified, while in patients with adrenocortical tumors, TLR10 , which is involved in the regulation of the innate immunity, was commonly modified. Our data show that using exome screening, it is possible to find signatures which correlate with the given clinical MEN1 outcomes, providing evidence that studies addressing modifier effects in MEN1 are reasonable.
Keyphrases
- genome wide
- copy number
- signaling pathway
- dna methylation
- cell adhesion
- electronic health record
- toll like receptor
- metabolic syndrome
- epithelial mesenchymal transition
- type diabetes
- single cell
- genome wide analysis
- brain injury
- adipose tissue
- transcription factor
- genome wide identification
- growth hormone
- weight loss
- sleep quality
- nuclear factor
- deep learning