KIT mutation detection in Tunisian patients with newly diagnosed myelogenous leukemia: prevalence and prognostic significance

Genetic Insights Into Leukemia Susceptibility in the Arab Population: A Scoping Review

As per the Global Cancer Observatory, the WHO Eastern Mediterranean region (which includes the Arabic countries) ranks highest for age-standardized mortality rate at 4 per 100,000, thus indicating a probable role of genetic associations. Identifying the genes associated with leukemia in the Arab population is crucial for effective preventive and treatment strategies. This scoping review aimed to determine the nature and extent of research available on the genes associated with the major types of leukemia among the Arab population. As per the scoping review guidelines, a comprehensive search was conducted in PUBMED and Google Scholar for articles published before 01/10/2023 and focused on leukemia-related genes among the Arab population. In total 119 studies, focusing on genes associated with leukemia met the inclusion criteria. On reviewing these studies, 27 genes were found to be associated with ALL, 33 genes with AML, seven genes with CLL, and 14 genes with CML. The majority of these genes were associated with an increased risk for the disease. Notably, the 119 studies covered only nine out of the 22 Arab countries, with 56 studies carried out in Egypt, exhibiting an imbalance in the regional distribution of the research landscape. Thus, indicating the inadequacy of research on leukemia genetics in the Arab region in comparison to the Western studies. This finding highlights the need for extensive research in the Middle Eastern region to gain geographically heterogeneous genetic information about the Arab population. In conclusion, this scoping study highlights the genes associated with the major types of leukemia among the Arab population and also indicates the need for comprehensive and regionally balanced research on leukemia genetics in Middle Eastern countries. Addressing this gap is essential to provide robust genetic data that can be used for targeted interventions to improve leukemia outcomes in the Middle East. Increased research efforts in all Middle Eastern countries will contribute to a greater understanding of genetic predisposition and help develop effective prevention strategies and treatments tailored to this population.

A Review of Cancer Genetics and Genomics Studies in Africa

Cancer is the second leading cause of death globally and is projected to overtake infectious disease as the leading cause of mortality in Africa within the next two decades. Cancer is a group of genomic diseases that presents with intra- and inter-population unique phenotypes, with Black populations having the burden of morbidity and mortality for most types. At large, the prevention and treatment of cancers have been propelled by the understanding of the genetic make-up of the disease of mostly non-African populations. By the same token, there is a wide knowledge gap in understanding the underlying genetic causes of, and genomic alterations associated with, cancer among black Africans. Accordingly, we performed a review of the literature to survey existing studies on cancer genetics/genomics and curated findings pertaining to publications across multiple cancer types conducted on African populations. We used PubMed MeSH terms to retrieve the relevant publications from 1990 to December 2019. The metadata of these publications were extracted using R text mining packages: RISmed and Pubmed.mineR. The data showed that only 0.329% of cancer publications globally were on Africa, and only 0.016% were on cancer genetics/genomics from Africa. Although the most prevalent cancers in Africa are cancers of the breast, cervix, uterus, and prostate, publications representing breast, colorectal, liver, and blood cancers were the most frequent in our review. The most frequently reported cancer genes were BRCA1, BRCA2, and TP53. Next, the genes reported in the reviewed publications’ abstracts were extracted and annotated into three gene ontology classes. Genes in the cellular component class were mostly associated with cell part and organelle part, while those in biological process and molecular function classes were mainly associated with cell process, biological regulation, and binding, and catalytic activity, respectively. Overall, this review highlights the paucity of research on cancer genomics on African populations, identified gaps, and discussed the need for concerted efforts to encourage more research on cancer genomics in Africa.

Integrated computational biology analysis to evaluate target genes for chronic myelogenous leukemia

Although hundreds of genes have been linked to chronic myelogenous leukemia (CML), many of the results lack reproducibility. In the present study, data across multiple modalities were integrated to evaluate 579 CML candidate genes, including literature‑based CML‑gene relation data, Gene Expression Omnibus RNA expression data and pathway‑based gene‑gene interaction data. The expression data included samples from 76 patients with CML and 73 healthy controls. For each target gene, four metrics were proposed and tested with case/control classification. The effectiveness of the four metrics presented was demonstrated by the high classification accuracy (94.63%; P<2x10‑4). Cross metric analysis suggested nine top candidate genes for CML: Epidermal growth factor receptor, tumor protein p53, catenin β 1, janus kinase 2, tumor necrosis factor, abelson murine leukemia viral oncogene homolog 1, vascular endothelial growth factor A, B‑cell lymphoma 2 and proto‑oncogene tyrosine‑protein kinase. In addition, 145 CML candidate pathways enriched with 485 out of 579 genes were identified (P<8.2x10‑11; q=0.005). In conclusion, weighted genetic networks generated using computational biology may be complementary to biological experiments for the evaluation of known or novel CML target genes.

Identifying and validating a combined mRNA and microRNA signature in response to imatinib treatment in a chronic myeloid leukemia cell line

Imatinib, a targeted tyrosine kinase inhibitor, is the gold standard for managing chronic myeloid leukemia (CML). Despite its wide application, imatinib resistance occurs in 20-30% of individuals with CML. Multiple potential biomarkers have been identified to predict imatinib response; however, the majority of them remain externally uncorroborated. In this study, we set out to systematically identify gene/microRNA (miRNA) whose expression changes are related to imatinib response. Through a Gene Expression Omnibus search, we identified two genome-wide expression datasets that contain expression changes in response to imatinib treatment in a CML cell line (K562): one for mRNA and the other for miRNA. Significantly differentially expressed transcripts/miRNAs post imatinib treatment were identified from both datasets. Three additional filtering criteria were applied 1) miRbase/miRanda predictive algorithm; 2) opposite direction of imatinib effect for genes and miRNAs; and 3) literature support. These criteria narrowed our candidate gene-miRNA to a single pair: IL8 and miR-493-5p. Using PCR we confirmed the significant up-regulation and down-regulation of miR-493-5p and IL8 by imatinib treatment, respectively in K562 cells. In addition, IL8 expression was significantly down-regulated in K562 cells 24 hours after miR-493-5p mimic transfection (p = 0.002). Furthermore, we demonstrated significant cellular growth inhibition after IL8 inhibition through either gene silencing or by over-expression of miR-493-5p (p = 0.0005 and p = 0.001 respectively). The IL8 inhibition also further sensitized K562 cells to imatinib cytotoxicity (p < 0.0001). Our study combined expression changes in transcriptome and miRNA after imatinib exposure to identify a potential gene-miRNA pair that is a critical target in imatinib response. Experimental validation supports the relationships between IL8 and miR-493-5p and between this gene-miRNA pair and imatinib sensitivity in a CML cell line. Our data suggests integrative analysis of multiple omic level data may provide new insight into biomarker discovery as well as mechanisms of imatinib resistance.