Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China.
Liaoning Key Laboratory of Molecular Targeted Anti-tumor Drug Development and Evaluation China Medical University, Shenyang, China.
Pathol Oncol Res. 2021 Apr 1;27:600727. doi: 10.3389/pore.2021.600727. eCollection 2021.
Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC. Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis. A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC. The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.
复杂的抗原加工和呈递过程参与了乳腺癌 (BC) 的发展和进展。单一的生物标志物不太可能充分反映免疫细胞和癌症之间的复杂相互作用;然而,很少有人试图找到一个稳健的与抗原加工和呈递相关的特征来预测 BC 患者的生存结果与肿瘤免疫学有关。因此,我们旨在开发一种基于免疫相关基因的准确基因特征,用于预测 BC 的预后。BC 患者的信息来自癌症基因组图谱。基因集富集分析用于确认与抗原加工和呈递相关的基因集,这些基因集促成了 BC。Cox 比例回归、多变量 Cox 回归和分层分析用于确定基因特征的预后能力。通过 KEGG 分析确定高风险组和低风险组之间差异表达的 mRNAs。一个由 HSPA5(热休克蛋白家族 A 成员 5)、PSME2(蛋白酶体激活亚基 2)和 HLA-F(主要组织相容性复合体,I 类,F)组成的三个基因特征与 OS 显著相关。HSPA5 和 PSME2 是保护性的(风险比 (HR) < 1),而 HLA-F 是有风险的(HR > 1)。风险评分、雌激素受体 (ER)、孕激素受体 (PR) 和 PD-L1 是独立的预后指标。KIT 和 ACACB 可能在基因特征调节 BC 预后的机制中具有重要作用。所提出的三个基因特征是估计 BC 患者生存结果的有前途的生物标志物。