Construction of the optimization prognostic model based on differentially expressed immune genes of lung adenocarcinoma
Autor: | Bin Zhao, Yang Zhai, Lina Li, Jingjin Li, Linhan Chang, Zijun Liao, Yuzhen Wang, Qian Chen, Xu Li |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Male
0301 basic medicine Oncology Lung adenocarcinoma Cancer Research Lung Neoplasms medicine.medical_treatment Datasets as Topic Kaplan-Meier Estimate Targeted therapy 0302 clinical medicine Surgical oncology Tumor Microenvironment Middle Aged Prognosis lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Neoplasm Proteins Gene Expression Regulation Neoplastic 030220 oncology & carcinogenesis Adenocarcinoma Immunohistochemistry Female Research Article Adult medicine.medical_specialty Adenocarcinoma of Lung Biology Optimization models Models Biological lcsh:RC254-282 Immunogenomics 03 medical and health sciences Internal medicine Biomarkers Tumor Genetics medicine Humans Lung cancer Aged Proportional Hazards Models Receiver operating characteristic Proportional hazards model Immunity Immunotherapy TCGA medicine.disease Gene Ontology 030104 developmental biology ROC Curve Transcriptome |
Zdroj: | BMC Cancer, Vol 21, Iss 1, Pp 1-13 (2021) BMC Cancer |
ISSN: | 1471-2407 |
Popis: | 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 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 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. |
Databáze: | OpenAIRE |
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