Department of Breast Surgery, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.
The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, China.
Immunol Res. 2024 Oct;72(5):1030-1050. doi: 10.1007/s12026-024-09505-5. Epub 2024 Jun 18.
Breast cancer remains the most common malignant carcinoma among women globally and is resistant to several therapeutic agents. There is a need for novel targets to improve the prognosis of patients with breast cancer. Bioinformatics analyses were conducted to explore potentially relevant prognostic genes in breast cancer using The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases. Gene subtypes were categorized by machine learning algorithms. The machine learning-related breast cancer (MLBC) score was evaluated through principal component analysis (PCA) of clinical patients' pathological statuses and subtypes. Immune cell infiltration was analyzed using the xCell and CIBERSORT algorithms. Kyoto Encyclopedia of Genes and Genomes enrichment analysis elucidated regulatory pathways related to speedy/RINGO cell cycle regulator family member C (SPDYC) in breast cancer. The biological functions and lipid metabolic status of breast cancer cell lines were validated via quantitative real-time polymerase chain reaction (RT‒qPCR) assays, western blotting, CCK-8 assays, PI‒Annexin V fluorescence staining, transwell assays, wound healing assays, and Oil Red O staining. Key differentially expressed genes (DEGs) in breast cancer from the TCGA and GEO databases were screened and utilized to establish the MLBC score. Moreover, the MLBC score we established was negatively correlated with poor prognosis in breast cancer patients. Furthermore, the impacts of SPDYC on the tumor immune microenvironment and lipid metabolism in breast cancer were revealed and validated. SPDYC is closely related to activated dendritic cells and macrophages and is simultaneously correlated with the immune checkpoints CD47, cytotoxic T lymphocyte antigen-4 (CTLA-4), and poliovirus receptor (PVR). SPDYC strongly correlated with C-C motif chemokine ligand 7 (CCL7), a chemokine that influences breast cancer patient prognosis. A significant relationship was discovered between key genes involved in lipid metabolism and SPDYC, such as ELOVL fatty acid elongase 2 (ELOVL2), malic enzyme 1 (ME1), and squalene epoxidase (SQLE). Potent inhibitors targeting SPDYC in breast cancer were also discovered, including JNK inhibitor VIII, AICAR, and JW-7-52-1. Downregulation of SPDYC expression in vitro decreased proliferation, increased the apoptotic rate, decreased migration, and reduced lipid droplets. SPDYC possibly influences the tumor immune microenvironment and regulates lipid metabolism in breast cancer. Hence, this study identified SPDYC as a pivotal biomarker for developing therapeutic strategies for breast cancer.
乳腺癌仍然是全球女性中最常见的恶性癌种，并且对几种治疗药物具有抗性。因此，需要寻找新的靶点来改善乳腺癌患者的预后。本研究通过使用癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）数据库，进行生物信息学分析，以探索乳腺癌中潜在的相关预后基因。通过机器学习算法对基因亚型进行分类。通过对临床患者病理状态和亚型的主成分分析（PCA）评估机器学习相关乳腺癌（MLBC）评分。使用 xCell 和 CIBERSORT 算法分析免疫细胞浸润。京都基因与基因组百科全书富集分析阐明了与乳腺癌中快速/RINGO 细胞周期调节因子家族成员 C（SPDYC）相关的调节途径。通过实时定量聚合酶链反应（RT-qPCR）、Western blot、CCK-8 检测、PI- Annexin V 荧光染色、Transwell 检测、划痕愈合检测和油红 O 染色，验证乳腺癌细胞系的生物学功能和脂代谢状态。筛选 TCGA 和 GEO 数据库中乳腺癌的关键差异表达基因（DEGs），并用于建立 MLBC 评分。此外，我们建立的 MLBC 评分与乳腺癌患者的不良预后呈负相关。进一步揭示并验证了 SPDYC 对乳腺癌肿瘤免疫微环境和脂代谢的影响。SPDYC 与激活的树突状细胞和巨噬细胞密切相关，同时与免疫检查点 CD47、细胞毒性 T 淋巴细胞抗原 4（CTLA-4）和脊髓灰质炎病毒受体（PVR）相关。SPDYC 与影响乳腺癌患者预后的趋化因子 C-C 基序趋化因子配体 7（CCL7）强烈相关。还发现了与 SPDYC 相关的关键脂代谢基因之间存在显著关系，如脂肪酸延长酶 2（ELOVL2）、苹果酸酶 1（ME1）和鲨烯环氧化酶（SQLE）。还发现了针对乳腺癌中 SPDYC 的有效抑制剂，包括 JNK 抑制剂 VIII、AICAR 和 JW-7-52-1。在体外下调 SPDYC 表达可降低增殖率，增加凋亡率，减少迁移并减少脂滴。SPDYC 可能影响肿瘤免疫微环境并调节乳腺癌中的脂代谢。因此，本研究确定 SPDYC 是开发乳腺癌治疗策略的关键生物标志物。
