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

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

doi:10.3389/fnagi.2023.1142163

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

Front Aging Neurosci. 2023 Mar 23:15:1142163. doi: 10.3389/fnagi.2023.1142163. eCollection 2023.

Authors

Xiaohong Qin^{1

2}, Shangfeng Yi³, Jingtong Rong^{2

4}, Haoran Lu^{1

2}, Baowei Ji¹, Wenfei Zhang¹, Rui Ding¹, Liquan Wu¹, Zhibiao Chen¹

Affiliations

¹ Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
² Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
³ Department of Neurosurgery, Enshi Center Hospital, Enshi, Hubei, China.
⁴ Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

Abstract

Introduction: Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. 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. 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.

Methods: First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender.

Results: Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. 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. The expression of ARGs did not differ by sex or age.

Discussion: 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.

Keywords: anoikis; immune infiltration; immunotherapy; ischemic stroke; machine learning; molecular cluster.