Expression of HLA class I and class II genes in patients with multiple skin warts.
Duygu GülserenÇağman Tanİsmail YazBegüm ÖzbekDeniz Çağdaşİlhan TezcanPublished in: Experimental dermatology (2021)
Human leukocyte antigens (HLAs), which are genetic markers that have critical roles in the immune response against pathogens, vary greatly among individuals. The aim of the study is to investigate the frequency of HLA class I (HLA-A, HLA-B and HLAC) and class II (HLA-DRB1, HLA-DQB1 and HLA-DQA1) genes in patients with multiple skin warts and to elucidate the role of these genes in the genetic susceptibility to skin warts. Peripheral venous blood samples were collected from 100 patients with multiple skin warts and 300 healthy individuals (controls). HLA typing was performed after DNA isolation from the blood samples. The HLA-A*02 (odds ratio [OR]: 0.12; p = 0.0019), HLA-DQA1*03:01 (OR: 0.45; p = 0.0017) and DQA1*05:01 (OR: 0.17; p < 0.0001) genes were significantly more prevalent in the patients than in the healthy individuals and were thus identified as risk genes. The HLA-DQA1*01:01 (OR: 0.17; p < 0.0001), HLA-DQA1*01:02 (OR: 0.17; p < 0.0001), HLA-DQA1*01:03 (OR: 0.11; p < 0.0001), HLA-DQA1*02:01 (OR:027; p<0.0001) and HLA-DQA1*05:05 (OR:0.16; p<0.0001) genes were classified as protective genes because of their low frequencies in the patients. The limitation of the study is that Human papillomavirus typing could not be performed while investigating the relationship between skin warts and HLA class I and class II genes. Our data suggest the role of HLA genes in the development of skin warts. However, other components of the major histocompatibility complex system and acquired factors of the immune system could also be involved and should be further investigated.
Keyphrases
- genome wide
- immune response
- end stage renal disease
- chronic kidney disease
- genome wide identification
- gene expression
- bioinformatics analysis
- endothelial cells
- soft tissue
- newly diagnosed
- ejection fraction
- toll like receptor
- prognostic factors
- genome wide analysis
- inflammatory response
- poor prognosis
- copy number
- long non coding rna
- machine learning
- transcription factor
- circulating tumor cells
- atomic force microscopy