Biology subjects

Malik, Z.

Publications and source records attributed to Malik, Z..

2 recordsLinked to original sources

bioRxiv2022

Vasoactive Endothelial Growth Factor and Heat Shock Protein Gene Expression Response in Kawasaki Disease Associated Coronary Arteritis

Kawasaki Disease (KD) is a childhood vasculitis primarily affecting medium-sized arteries, which can lead to severe complications, particularly with respect to coronary artery disease (CAD). The impact of thermal stress on KD coronary artery pathogenesis, in association with prolonged fever and inflammation, remains unclear. In this study, we hypothesized that altered gene expression (GE) of angiogenesis-inducing Heat Shock Proteins (HSPs) is associated with KD-CAD through pro-inflammation. Transcriptomic analysis was performed using the three largest KD peripheral blood studies in the clinical literature (KD1-KD3), and one study direct from coronary artery tissue (KD4). The analysis revealed a significant increase in TNF and NFKB1 GE, indicating the presence of inflammation based on gene expression profiles. Gene set enrichment analysis (GSEA) of KD1-KD3 datasets identified inflammatory pathways, including TNFA signaling via NFKB, IL6 JAK STAT 3 Signalling, and p53 (Heat Shock Protein 90). The study also focused on specific HSPs known to be associated with angiogenesis, namely HSPB1, HSPA1A, and HSP90AB1. The temporal transcript model (TTM) consistently showed up-regulation of pro-inflammatory genes VEGF-A, TNF, and NFKB1, as well as up-regulation of HSPA1A. GSEA revealed gene ontology pathways associated with VEGF production. These findings suggest that the binding of VEGF-A or VEGF-B to their receptors could potentially impact the coronary artery in KD. Additionally, the up-regulation of the gene HSPAB1 in KD has not been described previously. In contrast, KD4 showed no differential GE for the studied genes potentially related to end-stage KD. This study provides valuable insights into VEGF and HSPs in KD-associated inflammation. Future research should focus on developing a VEGF-HSP CAD model to explore implications for KD biomarking as well as developing precision management strategies.

genomics↗

bioRxiv2021

CDK1 and HSP90AA1 appears as novel regulatory gene in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network.

systems biology↗