Identification of a Risk Predictive Signature Based on Genes Associated with Tumor Size and Lymph Node Involvement in Breast Cancer
Identification of a Risk Predictive Signature Based on Genes Associated with Tumor Size and Lymph Node Involvement in Breast Cancer
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基于与乳腺癌肿瘤大小和淋巴结受累相关的基因识别风险预测特征
DOI：
10.1089/gtmb.2022.0124
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发表时间：
2022
影响因子：
1.4
通讯作者：
Xiufen Zhang
中科院分区：
文献类型：
--
作者：
Junqiang Wu;Yuqing Liu;Hu Huang;Mingjie Zhu;Xiufen Zhang
背景：乳腺癌是一种异质性疾病。乳腺癌中具有广泛淋巴结受累（STEL）的小肿瘤通常反映了生物学侵袭性表型和预后不良。本研究的目的是确定与STEL相关的关键基因，并研究其在乳腺癌中的预后价值。方法：从癌症基因组图谱（TCGA）数据库中获取乳腺癌标本的RNA序列数据进行差异分析。加权基因相关网络分析（WGCNA），以确定与肿瘤大小和淋巴结转移相关的共表达基因模块。利用基因集富集分析（GSEA）对所鉴定的基因进行生物学功能分析。结合LASSO和考克斯回归分析建立风险预测特征，并采用时间依赖性受试者工作特征（tdROC）和Kaplan-Meier分析评价其预测精度。采用定量RT-PCR来验证来自签名集的关键基因的表达水平。结果如下：WGCNA共鉴定出3个共表达基因模块中的2777个基因，转录组分析共鉴定出880个差异表达基因。两种方法鉴定的63个重叠基因被认为是STEL相关基因，并基于它们建立了9个基因的风险预测特征，3年、5年和7年的AUC分别达到0.810、0.811和0.753。结论：这项研究证明了STEL乳腺癌的转录组学特征，并成功建立了具有令人满意的准确性的风险预测特征。这些发现可能为乳腺癌的遗传病因学提供见解。
Background: Breast cancer is a heterogeneous disease. Small tumors with extensive lymph node involvement (STEL) in breast cancer often reflect a biologically aggressive phenotype and poor prognosis. The aim of this study was to identify key genes associated with STEL and investigate their prognostic values in breast cancer. Methods: RNA sequence data from breast cancer specimens were acquired from The Cancer Genome Atlas (TCGA) database for differential analysis. Weighted gene correlation network analyses (WGCNA) were performed to identify coexpressed gene modules associated with tumor size and lymph node metastases. Gene set enrichment analysis (GSEA) was employed to investigate the biological functions of the identified genes. A combination of LASSO and Cox regression analyses was conducted to establish a risk predictive signature, and time-dependent receiver operating characteristic (tdROC) and Kaplan-Meier analyses were used to evaluate its prediction precision. Quantitative RT-PCR was employed to validate the expression levels of the key genes from the signature set. Results: A total of 2777 genes from three coexpressed gene modules were identified by WGCNA, and 880 differentially expressed genes were identified by transcriptome analyses. The 63 overlapping genes identified by both methods were considered STEL-associated genes, and a 9-gene risk-predictive signature was established based on them, with AUCs at 3, 5, and 7 years reaching 0.810, 0.811, and 0.753, respectively. Conclusion: This study demonstrated the transcriptomic profile of STEL breast cancer and successfully established a risk predictive signature with satisfactory accuracy. These findings may provide insights in to the genetic etiology of breast cancer.