Identification of potential circadian genes and associated pathways in colorectal cancer progression and prognosis using microarray gene expression analysis.

Colorectal cancer (CRC) is third cancer causing death in the world. CRC is associated with disrupting the circadian rhythm (CR), closely associating the CRC progression and the dysregulation of genes involved in the biological clock. In this study, we aimed to understand the circadian rhythm changes in patients diagnosed with CRC. We used the GEO database with the ID GSE46549 for our analysis, which consists of 32 patients with CRC and one as normal control. Our study has identified five essential genes involved in CRC, HAPLN1, CDH12, IGFBP5, DCHS2, and DOK5, and had different enriched pathways, such as the Wnt-signaling pathway, at different time points of study. As a part of our study, we also identified various related circadian genes, such as CXCL12, C1QTNF2, MRC2, and GLUL, from the Circadian Gene Expression database, that played a role in circadian rhythm and CRC development. As circadian timing can influence the host tissue's ability to tolerate anticancer medications, the genes reported can serve as a potential drug target for treating CRC and become beneficial to translational settings.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Introduction: Osteosarcoma 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. Methods: In 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. Results: Our 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. Conclusion: To 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.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks.

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae.

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1-C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.