Interactions of Consanguinity and Number of Siblings with Childhood Acute Lymphoblastic Leukemia.
Ameer KakajeMohammad Marwan AlhalabiAyham GhareebBahjat KaramBassam MansourBayan ZahraOthman HamdanPublished in: BioMed research international (2020)
Acute lymphoblastic leukemia (ALL) is a common malignancy in children. Consanguinity has a high prevalence in developing countries and increases the probability of homozygosity for many genes which may affect ALL and its prognosis. We conducted a study to explore the impact of consanguinity and number of siblings on ALL as there are currently no studies to describe this effect. Data were collected from patients' records from the Children's University Hospital of Damascus University, which is the major cancer centre for children in Syria. This study included 193 children with ALL over one year. Number of siblings was not with the French-American-British (FAB) classification, gender, ALL subtype, or risk of ALL children. When comparing consanguinity degrees and complete blood counts at diagnosis, significant contradicting data were found in the third-degree and fourth-degree consanguinity when compared to one another and to not having consanguineous parents as third degree consanguinity was associated with normal platelets but lower WBC counts, and fourth-degree consanguinity was associated with normal haemoglobin levels and WBC counts, but lower platelet counts. Having consanguineous parents was also associated with acquiring ALL at an older age, L2 FAB classification, having a positive family history for malignancies, and not having hepatosplenomegaly (P < 0.05). Although L2 is known to be a poor prognosis indicatory, no association was found with consanguinity and risk. Finally, no association was found with ALL subtype or risk (P > 0.05). Although consanguinity and number of siblings have affected some variables and prognostic features of childhood ALL, the aetiology is not clear and we need further studies to clarify such an association as this will help in optimising therapy and accurately determine the risk.
Keyphrases
- acute lymphoblastic leukemia
- poor prognosis
- young adults
- machine learning
- intellectual disability
- deep learning
- end stage renal disease
- electronic health record
- ejection fraction
- long non coding rna
- stem cells
- chronic kidney disease
- gene expression
- autism spectrum disorder
- risk factors
- allogeneic hematopoietic stem cell transplantation
- prognostic factors
- transcription factor
- artificial intelligence
- red blood cell