Signaling factors potentially associated to the pathogenesis of Adult T-cell leukemia /lymphoma: A network-analysis and novel findings assessment.

Adult T-cell leukemia/lymphoma (ATLL) is a human T-cell leukemia virus (HTLV) type 1-associated disease of TCD4+ cell transformation. Despite extensive studies on ATLL development and progression, the fundamental processes of HTLV-1 oncogenicity are yet to be understood. This study aimed to integrate high-throughput microarray datasets to find novel genes involved in the mechanism of ATLL progression. For this purpose, five microarray datasets were downloaded from the Gene Expression Omnibus database and then profoundly analyzed. Differentially expressed genes and miRNAs were determined using the MetaDE package in the R software and the GEO2R web tool. The STRING database was utilized to construct the protein-protein interaction network and explore hub genes. Gene ontology and pathway enrichment analysis were carried out by employing the EnrichR web tool. Furthermore, flow cytometry was employed to assess the CD4/CD8 ratio, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the high-throughput data analysis results. Four miRNAs, including hsa-mir-146, hsa-mir-451, hsa-mir-31, and hsa-mir-125, were among the statistically significant differentially expressed miRNAs between healthy individuals and ATLL patients. Moreover, 924 differentially expressed genes were identified between normal and ATLL samples. Further network analysis highlighted 59 hub genes mainly regulating pathways implicated in viral interferences, immunological processes, cancer, and apoptosis pathways. Among the identified hub genes, RhoA and PRKACB were most considerable in the high-throughput analysis and were further validated by qRT-PCR. The RhoA and PRKACB expression were significantly down-regulated in ATLL patients compared to asymptomatic carriers (p<0.0001 and p=0.004) and healthy subjects (p=0.043 and p=0.002). Therefore, these corresponding miRNAs and proteins could be targeted for diagnosis purposes and designing effective treatments.