Research Paper Volume 16, Issue 2 pp 1463—1483

Identification and validation of anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer


Figure 1. Flowchart of the study. Thirty candidate lncRNAs were identified through Venn diagram analysis of differentially expressed lncRNAs, Kaplan-Meier survival analysis, and lncRNAs associated with ARGs (anoikis-related genes). Subsequently, a novel cancer signature model consisting of five prognostic arlncRNAs was developed for ovarian cancer patients using univariate Cox regression, LASSO analysis, and multivariate Cox regression analysis. The five arlncRNAs signature model was established and the patients were divided into two risk groups based on the risk scores. The accuracy and potential function of this signature were assessed through various analyses, including OS (overall survival) Kaplan-Meier analysis, risk plot analysis, ROC (receiver operating characteristic) curve analysis, nomogram construction, hallmark analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, GSEA (gene set enrichment analysis), and immune infiltrate analysis. OV refers to ovarian cancer, TCGA refers to The Cancer Genome Atlas, lncRNAs stands for long noncoding RNAs, DEGs represents differentially expressed genes, ARGs denotes anoikis-related genes, LASSO refers to least absolute shrinkage and selection operator, OS refers to overall survival, ROC stands for receiver operating characteristic, KEGG refers to Kyoto Encyclopedia of Genes and Genomes, and GSEA represents gene set enrichment analysis.