[Retracted] Identification of the Potential Biomarkers Involved in the Human Oral Mucosal Wound Healing: A Bioinformatic Study

Abstract

Objective. To identify the key genetic and epigenetic mechanisms involved in the wound healing process after injury of the oral mucosa. Materials and Methods. Gene expression profiling datasets pertaining to rapid wound healing of oral mucosa were identified using the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed to identify differentially expressed genes (DEGs) during oral mucosal wound healing. Next, functional enrichment analysis was performed to identify the biological processes (BPs) and signaling pathways relevant to these DEGs. A protein-protein interaction (PPI) network was constructed to identify hub DEGs. Interaction networks were constructed for both miRNA-target DEGs and DEGs-transcription factors. A DEGs-chemical compound interaction network and a miRNA-small molecular interaction network were also constructed. Results. DEGs were found significantly enriched in several signaling pathways including arachidonic acid metabolism, cell cycle, p53, and ECM-receptor interaction. Hub genes, GABARAPL1, GABARAPL2, HDAC5, MAP1LC3A, AURKA, and PLK1, were identified via PPI network analysis. Two miRNAs, miR-34a-5p and miR-335-5p, were identified as pivotal players in the miRNA-target DEGs network. Four transcription factors FOS, PLAU, BCL6, and RORA were found to play key roles in the TFs-DEGs interaction network. Several chemical compounds including Valproic acid, Doxorubicin, Nickel, and tretinoin and small molecular drugs including atorvastatin, 17β-estradiol, curcumin, and vitamin D3 were noted to influence oral mucosa regeneration by regulating the expression of healing-associated DEGs/miRNAs. Conclusion. Genetic and epigenetic mechanisms and specific drugs were identified as significant molecular mechanisms and entities relevant to oral mucosal healing. These may be valuable potential targets for experimental research.

A gene signature driven by abnormally methylated DEGs was developed for TP53 wild-type ovarian cancer samples by integrative omics analysis of DNA methylation and gene expression data

“…These findings imply that aberrant methylation is also a feature of OC; however, a comprehensive analysis of DNA methylation in this disease as well as reliable and stable diagnostic and prognostic classifiers modulated by DNA methylation are still lacking. Bioinformatics contributes to the research of targeted therapies for diseases (24,25). In this study, we performed differential analysis by integrating the DNA methylation and gene expression data from TP53 wild-type OC samples and normal samples to identify differentially expressed genes (DEGs) with abnormal methylation.…”

“…Bioinformatics contributes to the research of targeted therapies for diseases ( 24 , 25 ). In this study, we performed differential analysis by integrating the DNA methylation and gene expression data from TP53 wild-type OC samples and normal samples to identify differentially expressed genes (DEGs) with abnormal methylation.…”

Integrative Analysis of Deregulated miRNAs Reveals Candidate Molecular Mechanisms Linking H. pylori Infected Peptic Ulcer Disease with Periodontitis

“…In addition, such miRNA can serve as biomarkers or therapeutic drug target candidates [ 21 ]. Integrative bioinformatics has been applied successfully to decipher miRNA deregulation in oral diseases [ 22 ]. miRNAs have been identified as biomarkers of periodontal disease [ 23 ], and bioinformatic investigation has identified miR-125a-3p, miR-200a, and miR-142-3p as key deregulated miRNA in periodontitis [ 24 ].…”

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

“…Tumor mutation burden (TMB) and genomic instability have been revealed to be associated with the therapeutic response to immunotherapy ( 27 , 28 ). Bioinformatics contributes to the research of targeted therapies for diseases ( 29 , 30 ). Inspired by the previous study ( 31 ), we thought that we might be able to contribute to increasing the accuracy of immunotherapy by constructing novel molecular subtypes and a risk model based on the above TME-related or tumor-related pathways.…”