Xia Lei, Ye Zhen, Zheng Man, Tan Zhaofeng
Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Department of General Surgery, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Discov Oncol. 2025 May 4;16(1):668. doi: 10.1007/s12672-025-02393-7.
The intersection of aberrant iron metabolism and the rapidly advancing field of immunotherapy has emerged as a critical focus in breast cancer (BRCA) therapeutics. Ferroptosis, a distinct form of iron-dependent cell death driven by lipid peroxidation, has garnered increasing attention for its pivotal role in cancer progression.
Utilizing extensive datasets from TCGA and GEO, this research extracted a wealth of biological data, including mRNA splicing indices, genomic aberrations, copy number variations (CNV), tumor mutational burden (TMB), and diverse clinical information. Through precise Lasso regression analysis, this research constructed a prognostic model that elucidates the molecular interactions of FRGs in BRCA. Concurrent co-expression network analyses were performed to explore the dynamic interplay between gene expression patterns and FRGs, revealing potential regulatory mechanisms.
This research analysis revealed significant overexpression of FRGs in high-risk BRCA samples, highlighting their prognostic relevance beyond traditional clinical parameters. GSVA identified immune response and cancer-related pathways as predominantly active in high-risk groups, suggesting ferroptosis as a central modulator within the tumor microenvironment. Notably, genes such as ACTL8, VGF, and CPLX2 emerged as markers of tumorigenesis, while IL33 and TP63 were identified as potential key regulators of cancer progression, each exhibiting distinct expression profiles across risk levels. Furthermore, this research incorporated gene correlations, CNV profiles, SNP arrays, and drug susceptibility analyses, contributing to the advancement of precision oncology.
The integration of bioinformatics and machine learning in this study underscores a strong correlation between FRG expression patterns and BRCA prognosis, affirming their potential as precise biomarkers for personalized immunotherapy.
异常铁代谢与快速发展的免疫治疗领域的交叉点已成为乳腺癌（BRCA）治疗的关键焦点。铁死亡是一种由脂质过氧化驱动的独特形式的铁依赖性细胞死亡，因其在癌症进展中的关键作用而受到越来越多的关注。
本研究利用来自TCGA和GEO的大量数据集，提取了丰富的生物学数据，包括mRNA剪接指数、基因组畸变、拷贝数变异（CNV）、肿瘤突变负担（TMB）和各种临床信息。通过精确的套索回归分析，本研究构建了一个预后模型，阐明了BRCA中FRG的分子相互作用。同时进行共表达网络分析，以探索基因表达模式与FRG之间的动态相互作用，揭示潜在的调控机制。
本研究分析显示，FRG在高危BRCA样本中显著过表达，突出了它们在传统临床参数之外的预后相关性。GSVA确定免疫反应和癌症相关途径在高危组中主要活跃，表明铁死亡是肿瘤微环境中的核心调节因子。值得注意的是，ACTL8、VGF和CPLX2等基因成为肿瘤发生的标志物，而IL33和TP63被确定为癌症进展的潜在关键调节因子，它们在不同风险水平上均表现出独特的表达谱。此外，本研究纳入了基因相关性、CNV谱、SNP阵列和药物敏感性分析，有助于推进精准肿瘤学。
本研究中生物信息学与机器学习的整合强调了FRG表达模式与BRCA预后之间的强相关性，肯定了它们作为个性化免疫治疗精确生物标志物的潜力。
