No abstract

Background Adult T-cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T-cells in Human T-Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miRNAs) in various malignancies, the analysis of high-throughput miRNA data employing computational algorithms helps to identify potential biomarkers. Methods Weighted gene co-expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein–protein interaction networks (PPIN). Real-time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network. Results After constructing co-expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL-specific modules. Next, the hub miRNA including hsa-miR-18a-3p, has-miR-187-5p, hsa-miR-196a-3p, and hsa-miR-346 were found as hub miRNAs. The protein–protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse-transcriptase PCR in ATLL patients. Conclusion The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers.

Corona Virus Disease 2019 (COVID-19) has caused over six million deaths worldwide so far. COVID-19 has presented a variety of severities and outcomes which is able to damage many different organs. In this study, we aimed to identify factors responsible for severe illness and also alterations caused by the virus in various organs at the molecular level. First, after preprocessing steps, we chose one mRNA expression profile (GSE164805) for further analysis. Differentially Expressed Genes (DEGs) were screened with the Limma R package and considered for the PPI network construction. By maximizing co-expression value, we constructed subnetworks and subjected them to the Gene Sets Net Correlation Analysis (GSNCA). Successfully passed clusters were subjected to enrichment analysis. From 60k genes, 7106, 3151, and 1809 genes were considered as DEGs in normal vs. mild, normal vs. severe, and mild vs. severe comparisons, respectively, with p < 0.05 and |LogFC| > 2 as thresholds. PPI network analysis resulted in 17 modules, and 11 of them successfully passed GSNCA analysis with a P value < 0.05. Enrichment analysis culminated in identifying genes and signaling pathways with possible roles in the establishment of severe disease. We noticed considerable similarities between altered signaling pathways in COVID-19 and various malignancies. In addition, we detected alterations of pathways that can help to explain neurological involvement.