Identification of a Novel Germline PPP4R3A Missense Mutation Asp409Asn on Familial Non-Medullary Thyroid Carcinoma.
Yixuan HuZhuojun HanHonghao GuoNing ZhangNa ShenYujia JiangTao HuangPublished in: Biomedicines (2024)
Familial non-medullary thyroid carcinoma (FNMTC) accounts for 3% to 9% of all thyroid cancer cases, yet its genetic mechanisms remain unknown. Our study aimed to screen and identify novel susceptibility genes for FNMTC. Whole-exome sequencing (WES) was conducted on a confirmed FNMTC pedigree, comprising four affected individuals across two generations. Variants were filtered and analyzed using ExAC and 1000 Genomes Project, with candidate gene pathogenicity predicted using SIFT, PolyPhen, and MutationTaster. Validation was performed through Sanger sequencing in affected pedigree members and sporadic patients (TCGA database) as well as general population data (gnomAD database). Ultimately, we identified the mutant PPP4R3A (NC_000014.8:g.91942196C>T, or NM_001366432.2(NP_001353361.1):p.(Asp409Asn), based on GRCH37) as an FNMTC susceptibility gene. Subsequently, a series of functional experiments were conducted to investigate the impact of PPP4R3A and its Asp409Asn missense variant in thyroid cancer. Our findings demonstrated that wild-type PPP4R3A exerted tumor-suppressive effects via the Akt-mTOR-P70 S6K/4E-BP1 axis. However, overexpression of the PPP4R3A Asp409Asn mutant resulted in loss of tumor-suppressive function, ineffective inhibition of cell invasion, and even promotion of cell proliferation and migration by activating the Akt/mTOR signaling pathway. These results indicated that the missense variant PPP4R3A Asp409Asn is a candidate susceptibility gene for FNMTC, providing new insights into the diagnosis and intervention of FNMTC.
Keyphrases
- signaling pathway
- wild type
- copy number
- genome wide
- cell proliferation
- genome wide identification
- intellectual disability
- end stage renal disease
- single cell
- pi k akt
- dna methylation
- ejection fraction
- transcription factor
- early onset
- emergency department
- chronic kidney disease
- high throughput
- epithelial mesenchymal transition
- newly diagnosed
- prognostic factors
- photodynamic therapy
- electronic health record
- stem cells
- induced apoptosis
- autism spectrum disorder
- computed tomography
- oxidative stress
- escherichia coli
- big data
- magnetic resonance imaging
- cell therapy
- machine learning
- magnetic resonance
- endoplasmic reticulum stress
- deep learning
- pseudomonas aeruginosa
- dna damage
- artificial intelligence