NDLI: Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma

Content Provider	Springer Nature : BioMed Central
Author	Zhai, Yang Zhao, Bin Wang, Yuzhen Li, Lina Li, Jingjin Li, Xu Chang, Linhan Chen, Qian Liao, Zijun
Abstract	Background Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. Methods The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. Results In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. Conclusions In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-021-07911-8.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-021-07911-8
Journal	BMC Cancer
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-03-01
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Immunogenomics TCGA Lung adenocarcinoma Prognosis Optimization models Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

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