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Abstract Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortalities, characterized by substantial genetic heterogeneity that challenges a comprehensive understanding of its progression. This study employs next-generation sequencing data analysis to transform our comprehension of LUAD pathogenesis. Integrating epigenetic and transcriptomic data of LUAD patients, this approach assessed the critical regulatory occurrences, identified therapeutic targets, and offered profound insights into cancer molecular foundations. We employed the DNA methylation data to identify differentially methylated CpG sites and explored the transcriptome profiles of their adjacent genes. An intersectional analysis of gene expression profiles uncovered 419 differentially expressed genes (DEGs) influenced by smoke-induced differential DNA methylation, among which hub genes, including mitochondrial ribosomal proteins (MRPs), and ribosomal proteins (RPs) such asMRPS15,MRPS5,MRPL33,RPL24,RPL7L1,MRPL15,TUFM,MRPL22, andRSL1D1, were identified using a network-based approach. These hub genes were overexpressed and enriched to RNA processing, ribosome biogenesis, and mitochondrial translation, which is critical in LUAD progression. Enhancer Linking Methylation/Expression Relationship (ELMER) analysis revealed transcription factor (TF) binding motifs, such asJUN,NKX23,FOSB,RUNX3, andFOSL1, which regulated these hub genes through methylation-dependent enhancer dynamics. Predominant hypomethylation of MRPs and RPs disrupted mitochondrial function, contributed to oxidative phosphorylation (OXPHOS) and metabolic reprogramming, favoring cancer cell survival. The survival analysis validated the clinical relevance of these hub genes, with high-expression cohorts exhibiting poor overall survival (OS) outcomes enlightened their relevance in LUAD pathogenesis and presented the potential for developing novel targeted therapeutic strategies. 

Abstract Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA–TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein–Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations. 

Abstract Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. Here, we introduce MDRepo, a robust infrastructure that provides a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyber-infrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.