Department of Oncology, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, No.390 Huaihe Road, Luyang District, Hefei City, 230071, Anhui Province, China.
Department of Oncology, Wuhu Second People's Hospital, No. 66 Municipal Access Road, Wuhu City, 241000, Anhui Province, China.
J Cancer Res Clin Oncol. 2024 Oct 23;150(10):475. doi: 10.1007/s00432-024-05995-w.
Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.
The toolkit analyses involve techniques such as differential gene expression analysis, Gene Set Enrichment Analysis (GSEA), Weighted Co-Expression Network Analysis (WGCNA), and Machine Learning algorithms. Furthermore, in vitro cell experiments have demonstrated the impact of HSPB6 on cell migration, proliferation, and apoptosis.
The study identified multiple genes that displayed differential expression in breast cancer, notably FHL1 and HSPB6. A machine learning model was developed in this study and specifically trained for breast cancer diagnosis using these genes, achieving high precision. Furthermore, analysis of immune cell infiltration revealed an enrichment of Tregs and M2 macrophages in the treated group, showcasing its significant impact on the tumor's immunological context. A temporal analysis of breast cancer cells using single-cell RNA sequencing provided insights into cellular developmental trajectories and highlighted changes in expression patterns across key genes during disease progression. The upregulation of HSPB6 in MCF7 cells significantly inhibited both cell migration and proliferation abilities, suggesting that promoting HSPB6 expression could induce ferroptosis in breast cancer cells.
Our findings have identified compelling molecular targets and distinctive diagnostic markers for the clinical management of breast cancer. This data will serve as crucial guidance for further research in the field.
乳腺癌是全球重大公共卫生问题，是女性最常见的癌症，也是该病相关死亡的主要原因。推动乳腺癌进展的分子过程尚未完全阐明，突出了潜在生物学的复杂性及其对全球健康的关键影响。本研究的目的是对乳腺癌相关数据集进行生物信息学分析，以全面了解发挥作用的分子机制，并确定与该疾病相关的关键基因。
工具包分析涉及差异基因表达分析、基因集富集分析（GSEA）、加权共表达网络分析（WGCNA）和机器学习算法等技术。此外，体外细胞实验证明了 HSPB6 对细胞迁移、增殖和凋亡的影响。
该研究确定了多个在乳腺癌中显示差异表达的基因，特别是 FHL1 和 HSPB6。本研究开发了一个机器学习模型，专门使用这些基因进行乳腺癌诊断，具有很高的精度。此外，免疫细胞浸润分析表明，治疗组中 Tregs 和 M2 巨噬细胞丰富，展示了其对肿瘤免疫背景的重大影响。使用单细胞 RNA 测序对乳腺癌细胞进行的时间分析提供了对细胞发育轨迹的深入了解，并突出了关键基因在疾病进展过程中表达模式的变化。MCF7 细胞中 HSPB6 的上调显著抑制了细胞迁移和增殖能力，表明促进 HSPB6 表达可诱导乳腺癌细胞发生铁死亡。
我们的研究结果确定了用于乳腺癌临床管理的有吸引力的分子靶标和独特的诊断标志物。这些数据将为该领域的进一步研究提供重要指导。
