Immune-related long non-coding RNAs (irlncRNAs) appear valuable in predicting prognosis in patients with cancer. In this study, we used a fresh modeling algorithm to construct irlncRNAs signature and then assessed its predictive value for prognosis, tumor immune infiltration, and chemotherapy efficacy in gastric cancer (GC) patients.
The raw transcriptome data were extracted from the Cancer Genome Atlas (TCGA). Patients were randomly divided into the training and testing cohort. irlncRNAs were identified through co-expression analysis, after which differentially expressed irlncRNA (DEirlncRNA) pairs were identified. Next, we developed a model to distinguish between high- or low-risk groups in GC patients through univariate and LASSO regression analyses. A ROC curve was used to verify this model. After subgrouping patients according to the median risk score, we investigated the connection between the risk score of GC and clinicopathological characteristics. Functional enrichment analysis was also performed.
We find that the results indicate that immune-related lncRNA signaling has essential value in predicting prognosis, and it may be potential to measure the Efficacy for immunotherapy. This feature may be a guide to the selection of GC immunotherapy.
Our data revealed that immune-related lncRNA signaling had essential value in predicting prognosis, and it may be potentially used to measure the efficacy for immunotherapy. This feature may also be used to guide the selection of GC immunotherapy.