Department of Breast Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China.
Int J Oncol. 2022 Dec;61(6). doi: 10.3892/ijo.2022.5438. Epub 2022 Oct 12.
Breast cancer is the most prevalent type of cancer among women worldwide. The heterogeneous nature of breast cancer poses a serious challenge for prognostic prediction and individualized therapies. Recently, ferroptosis, an iron‑dependent form of programmed cell death, has been reported to serve a significant role in the regulation of the biological behavior of tumors. Several studies have revealed the prognostic significance of the ferroptosis‑related gene (FRG) model; however, additional efforts are required to elucidate the details. Moreover, genes that modulate ferroptosis may be promising candidate bioindicators in cancer therapy. The present study systematically assessed the expression profiles of FRGs to reveal the relationship between FRGs and the prognostic features of patients with breast cancer based on data obtained from the Gene Expression Omnibus and Molecular Taxonomy of Breast Cancer International Consortium. Using a non‑negative matrix factorization clustering method, patients with breast cancer were classified into two sub‑groups (cluster 1 and cluster 2) based on the expression of FRGs. Furthermore, Cox regression, and least absolute shrinkage and selection operator methods were used to construct a risk score formula comprised of nine genes, which stratified patients with breast cancer into two risk groups. Patients belonging to the high‑risk group exhibited significantly shorter overall survival (OS) time compared with patients in the low‑risk group. The prognostic value of this signature was further verified in the training and validation cohorts. The results for univariate and multivariate Cox regression analyses indicated that risk score acted as an independent predictor for OS. Subsequently, a nomogram was constructed. Receiver operating characteristic analysis further confirmed that the resulting nomogram exhibited powerful discriminatory ability. Functional analysis revealed that the immune environment differed notably between the two groups and indicated an association between ferroptosis and breast cancer proliferation, migration and drug resistance. Taken together, the present study demonstrated that FRGs were significantly associated with breast cancer progression, and thus could be used as novel biomarkers for prognostic prediction and individualized treatment of patients with breast cancer.
乳腺癌是全球女性中最常见的癌症类型。乳腺癌的异质性对预后预测和个体化治疗构成了严重挑战。最近，铁依赖性细胞程序性死亡形式的铁死亡已被报道在调节肿瘤的生物学行为中发挥重要作用。几项研究揭示了铁死亡相关基因（FRG）模型的预后意义；然而，需要进一步努力阐明细节。此外，调节铁死亡的基因可能是癌症治疗中有前途的候选生物标志物。本研究系统评估了 FRG 的表达谱，以根据从基因表达综合数据库和乳腺癌国际联合会分子分类学中获得的数据，揭示 FRG 与乳腺癌患者预后特征之间的关系。使用非负矩阵分解聚类方法，根据 FRG 的表达将乳腺癌患者分为两个亚组（簇 1 和簇 2）。此外，Cox 回归和最小绝对收缩和选择算子方法用于构建包含九个基因的风险评分公式，将乳腺癌患者分为两个风险组。属于高风险组的患者的总生存期（OS）明显短于低风险组的患者。该特征的预后价值在训练和验证队列中得到了进一步验证。单变量和多变量 Cox 回归分析的结果表明，风险评分是 OS 的独立预测因子。随后，构建了一个列线图。接收者操作特征分析进一步证实了所得到的列线图具有强大的区分能力。功能分析显示，两组之间的免疫环境明显不同，并表明铁死亡与乳腺癌增殖、迁移和耐药性之间存在关联。综上所述，本研究表明 FRG 与乳腺癌的进展显著相关，因此可作为预测乳腺癌患者预后和个体化治疗的新型生物标志物。
