Research Paper Volume 13, Issue 15 pp 19352—19374
Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database
- 1 Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- 2 Department of Gastroenterology, The Second Hospital of Shandong University, Jinan, China
- 3 Cheeloo College of Medicine, Shandong University, Jinan, China
- 4 Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- 5 Binzhou Medical University, Binzhou, China
Received: May 19, 2020 Accepted: July 6, 2021 Published: July 30, 2021https://doi.org/10.18632/aging.203333
How to Cite
Copyright: © 2021 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Bladder cancer (BLCA) is the major tumor of the urinary system, and immune-related genes (IRGs) contribute significantly to its initiation and prognosis.
Results: A total of 51 prognostic IRGs significantly associated with overall survival were identified. Functional enrichment analysis revealed that these genes were actively involved in tumor-related functions and pathways. Using multivariate Cox regression analysis, we detected 11 optimal IRGs (ADIPOQ, PPY, NAMPT, TAP1, AHNAK, OLR1, PDGFRA, IL34, MMP9, RAC3, and SH3BP2). We validated the prognostic value of this signature in two validation cohorts: GSE13507 (n = 165) and GSE32894 (n = 224). Furthermore, we performed a western blot and found that the expression of these IRGs matched their mRNA expression in TCGA. Moreover, correlations between risk score and immune-cell infiltration indicated that the prognostic signature reflected infiltration by several types of immune cells.
Conclusion: We identified and validated an 11-IRG-based risk signature that may be a reliable tool to evaluate the prognosis of BLCA patients and help to devise individualized immunotherapies.
Methods: Bioinformatics analysis was performed using TCGA and ImmPort databases. Cox regression was used to identify prognostic signatures. Two external GEO cohorts and western blotting of samples were performed to validate the IRG signature.
BLCA: bladder cancer; IRGs: immune-related genes; DEIRGs: differentially expressed immune-related genes; NMIBC: non muscle invasive bladder cancer; MIBC: muscle-invasive bladder cancer; TCGA: The Cancer Genome Atlas; FC: fold-change; TF: transcription factor; AUC: area under the curve; ROC: receiver operating characteristic; KM: Kaplan Meier.