Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes.
10.3779/j.issn.1009-3419.2023.102.31
- Author:
Jihong SUN
1
;
Hanwen ZHANG
1
;
Haoran LIU
1
;
Yuqing DONG
1
;
Pingyu WANG
1
Author Information
1. School of Public Health and Management, Binzhou Medical College, Yantai 264003, China.
- Publication Type:Journal Article
- Keywords:
Cuproptosis;
Drug therapy;
Immunity;
Lung neoplasms;
Prognosis model
- MeSH:
Humans;
Adenocarcinoma/genetics*;
Adenocarcinoma of Lung/genetics*;
Early Detection of Cancer;
Lung Neoplasms/genetics*;
Prognosis;
Copper;
Apoptosis
- From:
Chinese Journal of Lung Cancer
2023;26(8):591-604
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration.
METHODS:The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups.
RESULTS:A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy.
CONCLUSIONS:The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.
