Akhter, F.; Kahtani, F.H.A.; Sambawa, Z.M.; Alhassan, D.A.; AlSaif, R.A.; Haque, T.; Alam, M.K.; Hasan, M.T.; Islam, M.R.; Ahmed, K.; Basri, R. Explore Novel Biomarkers and Associated Key Pathways to Perform as Potential Prognostic Biomarkers and Therapeutic Targets in Oral Cancer. Preprints2022, 2022060183. https://doi.org/10.20944/preprints202206.0183.v1
APA Style
Akhter, F., Kahtani, F.H.A., Sambawa, Z.M., Alhassan, D.A., AlSaif, R.A., Haque, T., Alam, M.K., Hasan, M.T., Islam, M.R., Ahmed, K., & Basri, R. (2022). Explore Novel Biomarkers and Associated Key Pathways to Perform as Potential Prognostic Biomarkers and Therapeutic Targets in Oral Cancer. Preprints. https://doi.org/10.20944/preprints202206.0183.v1
Chicago/Turabian Style
Akhter, F., Kawsar Ahmed and Rehana Basri. 2022 "Explore Novel Biomarkers and Associated Key Pathways to Perform as Potential Prognostic Biomarkers and Therapeutic Targets in Oral Cancer" Preprints. https://doi.org/10.20944/preprints202206.0183.v1
Abstract
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.
Copyright:
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