Research Paper Volume 12, Issue 7 pp 5832—5857
Development of a prognostic index and screening of potential biomarkers based on immunogenomic landscape analysis of colorectal cancer
- 1 Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, P.R. China
Received: November 16, 2019 Accepted: March 3, 2020 Published: March 31, 2020
https://doi.org/10.18632/aging.102979How to Cite
Copyright © 2020 Lin 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.
Abstract
Background: Colorectal cancer (CRC) accounts for the highest fatality rate among all malignant tumors. Immunotherapy has shown great promise in management of many malignant tumors, necessitating the need to explore its role in CRC.
Results: Our analysis revealed a total of 71 differentially expressed IRGs, that were associated with prognosis of CRC patients. Ten IRGs (FABP4, IGKV1-33, IGKV2D-40, IGLV6-57, NGF, RETNLB, UCN, VIP, NGFR, and OXTR) showed high prognostic performance in predicting CRC outcomes, and were further associated with tumor burden, metastasis, tumor TNM stage, gender, age, and pathological stage. Interestingly, the IRG-based prognostic index (IRGPI) reflected infiltration of multiple immune cell types.
Conclusions: This model provides an effective approach for stratification and characterization of patients using IRG-based immunolabeling tools to monitor prognosis of CRC.
Methods: We performed a comprehensive analysis of expression profiles for immune-related genes (IRGs) and overall survival time in 437 CRC patients from the TCGA database. We employed computational algorithms and Cox regression analysis to estimate the relationship between differentially expressed IRGs and survival rates in CRC patients. Furthermore, we investigated the mechanisms of action of the IRGs involved in CRC, and established a novel prognostic index based on multivariate Cox models.