ArticleJournal, genetic engineering & biotechnology2024
Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease.
Article in Journal, genetic engineering & biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A hybrid machine learning framework with two-step feature selection for identifying key biomarkers and drug targets in monkeypox.Biochemistry and biophysics reports · 2026Article
- Global Salivary Proteomic Profiling of Individuals with the Co-Occurrence of Type 2 Diabetes Mellitus, Dyslipidemia, and Periodontitis.International journal of molecular sciences · 2025Article
- Integrative bioinformatics analysis unveils hub transcription factors and their interacting drugs in immunoglobulin A nephropathy: Implications for pathogenesis and treatments.Journal, genetic engineering & biotechnology · 2025Article
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5 authors.
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Abstract
aimDue to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.
methodsFour Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach.
resultsThese three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity.
conclusionPredictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.
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