Abstract
An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.
Highlights
As the most common malignant tumor in women, breast cancer (BC) resulted in 2,088,849 new cases and 626,679 deaths in 2018 [1]
Based on different artificial intelligence algorithms, the current study focused on developing artificial intelligence survival predictive systems for providing individual mortality risk prediction for BC patients
Bar plot (Figure 1C), Gene Ontology (GO) chord chart (Figure 2), and Kyoto Encyclopedia of Genes and Genomes (KEGG) chord plot (Supplementary Figure 2) showed that biological functions of immune genes were mainly enriched in leukocyte migration, positive regulation of cell adhesion, regulation of inflammatory response, regulation of immune effector process, T cell activation, regulation of lymphocyte activation, positive regulation of leukocyte cell–cell adhesion, leukocyte chemotaxis, positive regulation of cell–cell adhesion, and leukocyte cell–cell adhesion
Summary
As the most common malignant tumor in women, breast cancer (BC) resulted in 2,088,849 new cases and 626,679 deaths in 2018 [1]. Tumor-infiltrating immune cells were reported to be associated with tumorigenesis and prognosis [7, 8]. It was reported that there was a significant correlation relationship between tumor-infiltrating immune and prognosis in BC patients [9]. There were several prognostic models for prediction of prognosis in BC patients [11,12,13]. From a specific patient’s point of view, the patient’s own mortality risk prediction was more important than that of patients in different subgroups. A prognostic model that can provide individualized mortality risk prediction for a specific patient is helpful to optimize individualized treatment and improve clinical prognosis
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