背景及目标。对于患有乳腺癌（BC）的患者中邻近非肿瘤组织的预后作用仍不清楚。 邻近BC正常组织中免疫和标志基因集的活性变化可能在预测BC患者的预后方面扮演关键角色。 本研究的目的是基于肿瘤和邻近非肿瘤组织中免疫和标志基因集的活性变化，鉴定BC亚型和核糖体相关的预后基因，以改善患者的预后情况。 材料和方法。应用基因集变异分析（GSVA）评估整体样本免疫反应性的变化，并通过非负矩阵分解（NMF）鉴定三个免疫相关的BC亚型。在确定了最小绝对收缩和选择算子（LASSO）方法的预后基因集后，进行了KEGG（基因和基因组百科全书）和GO（基因本体论）分析。核糖体相关基因通过PPI（蛋白质 - 蛋白质相互作用）分析进行鉴定，并最终基于五个核糖体基因（RPS18，RPL11，PRLP1，RPL27A和RPL38）的表达构建了预后风险模型。 结果。对正常乳房组织和BC组织中免疫和标记基因组活性变化的综合分析鉴定出三个免疫相关的BC亚型。 BC亚型1具有最佳预后，亚型3具有最差的总生存率。我们通过最小绝对值收缩和选择算子（LASSO）法在非肿瘤组织中鉴定出预后基因组。我们发现KEGG和GO分析的结果与核糖体相关基因的结果无法区分。最后，我们确定与核糖体相关的基因表现出作为乳腺癌患者总体生存率可靠预测因子的潜力。 结论。我们的研究为BC的治疗提供了重要的指导。在乳房切除术后，应彻底评估BC组织和邻近非肿瘤组织中基因集活性的变化，特别关注非肿瘤组织中与核糖体相关的基因的变化。

Background and Objectives. The prognostic role of adjacent nontumor tissue in patients with breast cancer (BC) is still unclear. The activity changes in immunologic and hallmark gene sets in normal tissues adjacent to BC may play a crucial role in predicting the prognosis of BC patients. The aim of this study was to identify BC subtypes and ribosome-associated prognostic genes based on activity changes of immunologic and hallmark gene sets in tumor and adjacent nontumor tissues to improve patient prognosis. Materials and Methods. Gene set variation analysis (GSVA) was applied to assess immunoreactivity changes in the overall sample and three immune-related BC subtypes were identified by non-negative matrix factorization (NMF). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses were after determining the prognostic gene set using the least absolute shrinkage and selection operator (LASSO) method. Ribosome-related genes were identified by PPI (protein-protein interaction) analysis, and finally a prognostic risk model was constructed based on the expression of five ribosomal genes (RPS18, RPL11, PRLP1, RPL27A, and RPL38). Results. A comprehensive analysis of immune and marker genomic activity changes in normal breast tissue and BC tissue identified three immune-related BC subtypes. BC subtype 1 has the best prognosis, and subtype 3 has the worst overall survival rate. We identified a prognostic gene set in nontumor tissue by the least absolute shrinkage and selection operator (LASSO) method. We found that the results of both KEGG and GO analyses were indistinguishable from those of ribosome-associated genes. Finally, we determined that genes associated with ribosomes exhibit potential as a reliable predictor of overall survival in breast cancer patients. Conclusions. Our research provides an important guidance for the treatment of BC. After a mastectomy, the changes in gene set activity of both BC tissues and the nontumor tissues adjacent to it should be thoroughly evaluated, with special attention to changes in ribosome-related genes in the nontumor tissues.