Ruan Zhaohui, Chi Dongmei, Wang Qianyu, Jiang Jiaxin, Quan Qi, Bei Jinxin, Peng Roujun
VIP Section Department, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.
Department of Anesthesiology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ann Transl Med. 2022 Dec;10(24):1333. doi: 10.21037/atm-22-5684.
Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.
The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.
Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.
Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).
乳腺癌是全球女性中最常见的恶性肿瘤。其特征在于复杂的肿瘤微环境（TME），其中不同类型的细胞错综复杂地组合在一起，这在筛选肿瘤细胞相关特征或构建基因共表达网络时可能会造成混淆。最近出现的单细胞RNA测序（scRNA-seq）是研究复杂TME中细胞组学变化的有效方法。
通过对58个样本的公共scRNA-seq数据进行全面分析，筛选出恶性上皮细胞的失调基因。基于恶性细胞的scRNA-seq数据进行共表达和基因集富集分析（GSEA），以阐明这些失调基因的潜在功能。使用迭代LASSO-Cox在这些失调基因中进行第二轮筛选以构建风险组。最后，基于风险分组和其他临床特征构建乳腺癌预后预测模型。
我们的结果表明了恶性乳腺癌上皮细胞的1262个基因的转录特征。为了评估这些基因在乳腺癌中的功能，我们还在单细胞分辨率下构建了这些失调基因的共表达网络，并使用300多个已发表的转录组学数据集和31个成簇规律间隔短回文重复序列（CRISPR）筛选数据集进一步验证了结果。此外，我们基于scRNA-seq和批量测序数据集开发了一个可靠的预测模型。
我们的研究结果为乳腺癌进展过程中的转录组学和基因共表达网络提供了见解，并提出了治疗乳腺癌的潜在候选生物标志物和治疗靶点。我们的结果可通过网络应用程序（https://prognosticpredictor.shinyapps.io/GCNBC/）获取。
