Association of Chromosome 17 Aneuploidy, TP53 Deletion, Expression and Its rs1042522 Variant with Multiple Myeloma Risk and Response to Thalidomide/Bortezomib Treatment.
Sylwia Popek-MarciniecWojciech StykMagdalena Wojcierowska-LitwinSylwia ChocholskaAneta Szudy-SzczyrekMarzena SamardakiewiczGrazyna Swiderska-KolaczJoanna Czerwik-MarcinkowskaSzymon ZmorzynskiPublished in: Cancers (2023)
Multiple myeloma (MM) is a multifactorial genetic disorder caused by interactive effects of environmental and genetic factors. The proper locus of the TP53 gene (17p13.1) and its protein is essential in genomic stability. The most common variant of the TP53 gene-p.P72R (rs1042522)-shows functional variation. The aim of our study was a complex analysis of the TP53 p.P72R variant and TP53 gene expression in relation to chromosomal changes of the TP53 gene locus , as well as MM risk and outcome. Genomic DNA from 129 newly diagnosed MM patients was analyzed by methods of automated DNA sequencing (for TP53 variant analysis) and cIg-FISH (for chromosomal aberrations analysis). RNA was used in real-time PCR to determine the TP53 expression. In MM patients, the TP53 variant was not in Hardy-Weinberg equilibrium. The RR genotype was associated with lower MM risk (OR = 0.44, p = 0.004). A higher number of plasma cells was found in patients with RR genotype in comparison to those with PP + PR genotypes (36.74% vs. 28.30%, p = 0.02). A higher expression of the TP53 gene was observed in PP + PR genotypes vs. RR homozygote ( p < 0.001), in smokers vs. non-smokers ( p = 0.02). A positive Pearson's correlation was found between the TP53 expression level and the number of plasma cells (r = 0.26, p = 0.04). The presence of chromosome 17 aberrations with or without TP53 locus did not affect the MM risk and outcome. Similar results were observed in the case of TP53 gene expression and the p.P72R variant.
Keyphrases
- copy number
- newly diagnosed
- gene expression
- multiple myeloma
- poor prognosis
- genome wide
- dna methylation
- ejection fraction
- induced apoptosis
- binding protein
- prognostic factors
- deep learning
- risk assessment
- circulating tumor
- cell death
- single molecule
- signaling pathway
- circulating tumor cells
- genome wide identification
- replacement therapy