Ding Shuning, Sun Xi, Zhu Li, Li Yafen, Chen Weiguo, Shen Kunwei
Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Int Immunopharmacol. 2021 Nov;100:108122. doi: 10.1016/j.intimp.2021.108122. Epub 2021 Sep 15.
In the view that immune-related genes play a crucial role in breast cancer progression and long-term patient outcomes, we aimed to identify a novel gene signature based on immune-related genes to improve the prognostic prediction of breast cancer.
RNA sequencing data and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate and multivariate Cox regression analyses were conducted to establish the immune-related prognostic signature (IRPS). Then, the IRPS was validated by Kaplan-Meier analyses, time-dependent ROC curve analyses and multivariate Cox regression analyses. External validation was conducted in GSE96058. Nomogram combining IRPS with clinical factors was developed and then validated by time-dependent ROC curve analyses and calibration plots. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression level of immune-related genes in tumor and normal tissues.
The IRPS based on 4 immune-related genes (CCL1, VGF, TSLP, FABP9) were constructed. Patients in the low-risk group had significantly better overall survival than those in the high-risk group (p = 0.0011 in the training set, p = 0.0043 in the validation set, p < 0.0001 in the entire set, p < 0.001 in the external validation set). Multivariate analyses indicated that IRPS could independently predict OS in the training set (HR, 0.48; 95% CI, 0.24-0.83; p = 0.009), validation set (HR, 0.55; 95% CI, 0.34-0.90; p = 0.018), entire set (HR, 0.52; 95% CI, 0.36-0.75; p < 0.001) and external validation set (HR: 0.74, 95% CI: 0.59-0.92, p = 0.007). Sequentially, we establish a nomogram by integrating IRPS and clinical factors, which showed satisfactory predictive performance with 3-year, 5-year, 10-year AUC of 0.701, 0.706 and 0.694. Results of qRT-PCR validated that higher expression level of FABP9, CCL1 and VGF and lower expression level of TSLP in tumor samples compared to normal tissues.
Collectively, a four-gene based IRPS was developed and validated for patients with breast cancer. As an independent and robust predictor, the IRPS was constructive to risk stratification of breast cancer.
鉴于免疫相关基因在乳腺癌进展和患者长期预后中起关键作用,我们旨在基于免疫相关基因鉴定一种新的基因特征,以改善乳腺癌的预后预测。
从癌症基因组图谱(TCGA)获取RNA测序数据和临床信息。进行单变量和多变量Cox回归分析以建立免疫相关预后特征(IRPS)。然后,通过Kaplan-Meier分析、时间依赖性ROC曲线分析和多变量Cox回归分析对IRPS进行验证。在GSE96058中进行外部验证。开发了将IRPS与临床因素相结合的列线图,然后通过时间依赖性ROC曲线分析和校准图进行验证。进行定量实时聚合酶链反应(qRT-PCR)以验证肿瘤组织和正常组织中免疫相关基因的表达水平。
构建了基于4个免疫相关基因(CCL1、VGF、TSLP、FABP9)的IRPS。低风险组患者的总生存期明显优于高风险组患者(训练集p = 0.0011,验证集p = 0.0043,整个数据集p < 0.0001,外部验证集p < 0.001)。多变量分析表明,IRPS可在训练集(HR,0.48;95% CI,0.24 - 0.83;p = 0.009)、验证集(HR,0.55;95% CI,0.34 - 0.90;p = 0.018)、整个数据集(HR,0.