Background: Acute myeloid leukemia (AML) is a cancer arising in the bone marrow and is the most common type of adult leukemia. AML has a poor prognosis, and currently, its prognosis evaluation does not include immune status assessment. This study established an immune-related long non-coding RNA (lncRNA) prognostic risk model for AML based on immune lncRNAs screening.
Methods: To construct training and validation cohorts, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases were accessed to obtain gene expression profiles and clinical data. The correlation between lncRNAs and immunity genes was analyzed using the "limma" package, and the immune-related lncRNAs were obtained. Through least absolute shrinkage and selection operator regression, a prognostic model was established with immune-related lncRNAs. Using the median risk score, patients were divided into high- and low-risk groups. The Kaplan-Meier method was used for survival analysis, whereas the accuracy of the risk model was evaluated using time-dependent receiver operating characteristic curves, risk score distribution, survival status, and risk heat maps. We utilized univariate and multivariate Cox regression to examine the association between risk score and clinical variables and AML survival and prognosis.
Results: In the immune-related lncRNA prognostic risk model, the prognosis was better for low-risk than for high-risk patients, indicating risk score of this model as an independent indicator of prognosis. The area under the curve value for 1-, 3-, and 5-year survival of TCGA patients was 0.817, 0.859, and 0.909, respectively, whereas that of GEO patients (of dataset GPL96-GSE37642) was 0.603, 0.652, and 0.624, respectively. Gene set enrichment analysis revealed the enrichment of multiple pathways, such as antigen processing, B-cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, and chemokines, in high-risk patients.
Conclusions: In this study, immune-related lncRNA prognostic risk models effectively predicted AML survival and provided potential treatment targets.
Keywords: Acute myeloid leukemia (AML); immunology; long non-coding RNA (lncRNA); prognosis; risk model.
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