列线图
肿瘤科
比例危险模型
医学
危险系数
内科学
结直肠癌
队列
置信区间
Lasso(编程语言)
预测模型
癌症
总体生存率
计算机科学
万维网
作者
Xiaojuan Zhao,Jianzhong Liu,Shuzhen Liu,Fangfang Yang,Erfei Chen
出处
期刊:Cancers
[Multidisciplinary Digital Publishing Institute]
日期:2019-11-04
卷期号:11 (11): 1722-1722
被引量:23
标识
DOI:10.3390/cancers11111722
摘要
Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26–2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20–3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.
科研通智能强力驱动
Strongly Powered by AbleSci AI