Identification of hub genes and key pathways associated with angioimmunoblastic T-cell lymphoma using weighted gene co-expression network analysis.

Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive subtype of peripheral T-cell lymphoma (PTCL) that has a poor 5-year overall survival rate due to its lack of precise therapeutic targets. Identifying potential prognostic markers of AITL may provide information regarding the development of precision medicine. RNA sequence data from PTCL and patient clinic traits were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify DEGs between the different PTCL subtypes and investigate the relationship underlying co-expression modules and clinic traits. Gene ontology (GO) and protein-protein interaction (PPI) network analyses based on DAVID and the STRING website, respectively, were utilized to deeply excavate hub genes. After removing the outliers from the GSE65823, GSE58445, GSE19069, and GSE6338 datasets using the results from an unsupervised cluster heatmap, 50 AITL samples and 55 anaplastic large cell lymphoma (ALCL) samples were screened. A total of 677 upregulated DEGs and 237 downregulated DEGs were identified in AITL and used to construct a PPI network complex. Using WGCNA, 12 identified co-expression modules were constructed from the 5468 genes with the top 10% of variance, and 192 genes from the Turquoise and Brown modules were with a Gene Significance (GS) cut-off threshold >0.6. Eleven hub genes (CDH1, LAT, LPAR1, CXCL13, CD27, ICAM2, CD3E, CCL19, CTLA-4, CXCR5, and C3) were identified. Only CTLA-4 overexpressed was found to be a poor prognostic factor according to survival analysis. Gene set enrichment analysis (GSEA) identified and validated the intersection of key pathways (T cell receptor, primary immunodeficiency, and chemokine signaling pathways). Our findings provide the framework for the identification of AITL co-expression gene modules and identify key pathways and driving genes that may be novel treatment targets and helpful for the development of a prognostic evaluation index.