AUTHOR=Qin Xiaohong , Yi Shangfeng , Rong Jingtong , Lu Haoran , Ji Baowei , Zhang Wenfei , Ding Rui , Wu Liquan , Chen Zhibiao 

TITLE=Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 15 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1142163

DOI=10.3389/fnagi.2023.1142163

ISSN=1663-4365

ABSTRACT=Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Although tPA is the main treatment option for IS, a multitude of IS patients are at high risk of severe brain injury due to the narrow therapeutic time window, reperfusion injury and rebleeding complications. Hence, it is urgent to explore the molecular mechanisms following stroke and identify novel signature genes or biomarkers, so as to provide insights into the etiology of IS as well as potential therapeutic targets, and promote the development of new therapeutic approaches. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Therefore, it is essentially an apoptotic process. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored. In this study, we systematically evaluated anoikis-related genes (ARGs) in IS. First, we systematically explored the expression profile of ARGs in IS and normal control samples. Then, we performed consensus clustering, immuno-infiltration analysis and functional enrichment analysis on IS samples using the differentially expressed genes (DEGs) of anoikis. In addition, we conducted machine learning to screen five signature genes (AKT1, BRMS1, PTRH2, TFDP1 and TLE1) of IS. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. Finally, we identified the expression patterns of ARGs in IS patients across age or gender. This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.