Hematopoietic stem cell (HSC) transplantation (HSCT) is used to treat various hematologic disorders. Use of genetically modified mouse models of hematopoietic cell transplantation have been critical in our fundamental understanding of HSC biology and developing approaches for human patients. Pre-clinical studies in animal models provide insight into the journey of transplanted HSCs from infusion to engraftment in bone marrow (BM) niches. Various signaling molecules and growth factors secreted by HSCs and the niche microenvironment play critical roles in homing and engraftment of the transplanted cells. The sustained equilibrium of these chemical and biologic factors ensures that engrafted HSCs generate healthy and durable hematopoiesis. Transplanted healthy HSCs compete with residual host cells to repopulate stem cell niches in the marrow. Stem cell niches, in particular, can be altered by the effects of previous treatments, aging, and paracrine effects of leukemic cells that create inhospitable bone marrow niches that are unfavorable for healthy hematopoiesis. More work to understand how stem cell niches can be restored to favour normal hematopoieis may be key to reducing leukemic relapses following transplant.

Background: Oral cancer (OC) is serious health concerning issue that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. Methods: Publicly available repository the Gene Expression Omnibus (GEO) was responsible to collect OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected to apply analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to figure out the up-regulated and down-regulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and Pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein-protein interaction (PPI) networks were built to identify the most performed genes, clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study deeply about OC-associated DEGs. Finally, top gene performers from PPI networks were used to apply drug signature analysis. Results: After applying filtration and cut-off criteria 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the Cell cycle and Influenza A. The PPI network holds 88 nodes and 492 edges and CDC6 had the highest number of connections. 4 clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of the pathophysiology and the development of new therapies for OC.