Fan Xiaokai, Xin Le, Yu Xuan, Liu Maoxuan, Shim Joong Sup, Yang Gui, Chen Liang
Department of General Surgery/Otolaryngology, Longgang Central Hospital, Shenzhen, China.
Shenzhen Laboratory of Tumor Cell Biology, Institutes of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Mol Divers. 2024 Dec 10. doi: 10.1007/s11030-024-11035-z.
Breast cancer is a leading cause of cancer mortality among women globally, with over 2.26 million new cases annually, according to GLOBOCAN 2020. This accounts for approximately 25% of all new female cancers and 15.5% of female cancer deaths. To address this critical public health challenge, we conducted a multi-omics study aimed at identifying hub genes, therapeutic targets, and potential natural product-based therapies. We employed weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis to pinpoint hub genes in breast cancer. Regulatory networks for these genes were constructed by re-analyzing chromatin immunoprecipitation sequencing (ChIP-seq) data from breast cancer cell lines. Additionally, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were utilized to characterize hub gene expression profiles and their relationships with immune cell clusters and tumor microenvironments. Survival analysis based on mRNA and protein expression levels identified prognostic factors and potential therapeutic targets. Lastly, large-scale virtual screening of natural product compounds revealed leading compounds that target squalene epoxidase (SQLE). Our multi-omics analysis paves the way for more effective clinical treatments for breast cancer.
根据2020年全球癌症负担数据(GLOBOCAN),乳腺癌是全球女性癌症死亡的主要原因,每年新增病例超过226万。这约占所有女性新增癌症病例的25%,以及女性癌症死亡人数的15.5%。为应对这一严峻的公共卫生挑战,我们开展了一项多组学研究,旨在识别关键基因、治疗靶点以及潜在的基于天然产物的治疗方法。我们采用加权基因共表达网络分析(WGCNA)和差异基因表达分析来确定乳腺癌中的关键基因。通过重新分析乳腺癌细胞系的染色质免疫沉淀测序(ChIP-seq)数据,构建了这些基因的调控网络。此外,利用单细胞RNA测序(scRNA-seq)和空间转录组学(ST)来表征关键基因的表达谱及其与免疫细胞簇和肿瘤微环境的关系。基于mRNA和蛋白质表达水平的生存分析确定了预后因素和潜在的治疗靶点。最后,对天然产物化合物进行大规模虚拟筛选,发现了靶向角鲨烯环氧酶(SQLE)的先导化合物。我们的多组学分析为乳腺癌更有效的临床治疗铺平了道路。