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
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
Externí odkaz: