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One of the biggest threats to public health in the 21st century is antimicrobial resistance (AMR), which happens when bacteria change and make antibiotics that treat infections less effective. Understanding the AMR genes that impart the resistance to multidrug resistance is vital to comprehend the issue. Campylobacter jejuni, which has AMR genes, is the focus of our study. For the evolutionary examination of the genes and their variations, phylogenetic analysis has been conducted. To further our insight, The construction and analysis of a gene interaction network with 39 functional relationships. The clustering analysis revealed two interconnected clusters (C1, C2) that are associated with AMR processes. In functional enrichment analysis, Cellular components (CCs), Molecular Functions (MFs), and Biological Processes (BPs) were observed to have massive involvement. Resistance is inherited by Gram-positive bacteria via mutations in the rpl genes, which code for ribosomal proteins. The gene network analysis revealed genes rplE, rplV, rplG, rplK, rplA, rplJ, rpsE, rplB, rpsL, and rpmA interacting with their functional counterparts as frequently as feasible and can be considered as hub genes. Understanding the molecular mechanisms of AMR can be achieved by analyzing the ribosome pathway’s enriched KEGG pathway is essential for comprehending antibiotic resistance.A further benefit of the study is that it may help researchers create new drug-resistant strains of bacteria treatments for C. jejuni.

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IntroductionOsteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease.MethodsIn our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks.ResultsOur study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc.ConclusionTo develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.