An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer

列线图 肿瘤科 比例危险模型 单变量 内科学 多元统计 膀胱癌 医学 基因签名 生存分析 多元分析 接收机工作特性 Lasso(编程语言) 癌症 基因 生物 基因表达 统计 万维网 生物化学 计算机科学 数学
作者
Qing Liu,Yunchao Wang,Huayu Gao,Fahai Sun,Xuan Wang,Huawei Zhang,Jianning Wang
出处
期刊:Frontiers in Genetics [Frontiers Media]
卷期号:13 被引量:3
标识
DOI:10.3389/fgene.2022.837301
摘要

Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the survival of patients with bladder cancer. Method: The differentially expressed immunologic genes (DEGs) are identified in The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) was used to stratify the DEGs in TCGA. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox analysis to establish a prognostic risk model. A nomogram was used to establish an individualized prognostic signature for predicting survival. The potential pathways underlying the model were explored. Results: A total of 1,018 DEGs were screened. All samples were divided into two clusters (C1 and C2) by NMF with different immune cell infiltration, and the C2 subtype had poor prognosis. We constructed a 15-gene prognostic risk model from TCGA cohort. The patients from the high-risk group had a poor overall survival rate compared with the low-risk group. Time-dependent ROC curves demonstrated good predictive ability of the signature (0.827, 0.802, and 0.812 for 1-, 3-, and 5-year survival, respectively). Univariate and multivariate Cox regression analyses showed that the immunologic prognostic risk model was an independent factor. The decision curve demonstrated a relatively good performance of the risk model and individualized prognostic signature, showing the best net benefit for 1-, 3-, and 5-year OS. Gene aggregation analysis showed that the high-risk group was mainly concentrated in tumorigenesis and migration and immune signaling pathways. Conclusion: We established a risk model and an individualized prognostic signature, and these may be useful biomarkers for prognostic prediction of patients with bladder cancer.

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